subject:(driver modeling)

University of Michigan

1. Liu, Ke. Measuring and Quantifying Driver Workload on Limited Access Roads.

Degree: PhD, Industrial & Operations Engineering, 2019, University of Michigan

URL: http://hdl.handle.net/2027.42/151422

► Minimizing driver errors should improve driving safety. Driver errors are more common when workload is high than when it is low. Thus, it is of… (more)

▼ Minimizing driver errors should improve driving safety. Driver errors are more common when workload is high than when it is low. Thus, it is of great importance to study driver workload. Knowing the amount of workload at any given time, take-over time can be determined, adaptive in-vehicle systems can be refined, and distracting in-vehicle secondary tasks can be regulated. In this dissertation, a model quantifying workload as a function of traffic, in which workload is proportional to inverse time headway (THW) and time to collision (TTC), was proposed. Two experiments were conducted to investigate how traffic affected driver workload and evaluate the proposed model. The driving scenarios were categorized into static (i.e., no relative movements among vehicles) and dynamic (i.e., there are relative velocities and lane change actions). Three categories of workload measures (i.e., workload rating, occlusion %, and driving performance statistics) were analyzed and compared. A GOMS model was built based upon a timeline model by using timerequired to represent mental resources demanded and timeavailable to represent mental resources available. In static traffic, the workload rating increased with increased number of vehicles around but was unaffected by participant age. The workload ratings decreased with increasing Distance Headways (DHWs) of each vehicle. From greatest to least, the effects were: DHWLead, DHWLeftLead, DHWLeftFollow, DHWFollow. Any surrounding vehicle that was 14.5 m away from the participant resulted in significant greater workload. Drivers tended to compromise longitudinal speed but still maintain lateral position when workload increased. Although occlusion% was less sensitive to scenarios having no lead vehicles, it can nonetheless be well predicted using the proposed workload model in sensitive scenarios. The resulting equations were occlusion% = 0.35 + 0.05/THWLead + 0.02/THWLeftLead - 0.08Age (Rocclusion2=0.91); rating = 1.74 + 1.74/THWLead + 0.20/THWFollow + 0.79/THWLeftLead + 0.28/THWLeftFollow (Rrating2=0.73). In dynamic traffic, drivers experienced greater workload in the faster lane; higher workload level was associated with greater relative velocity between two lanes. Both rating and occlusion% can be described using the proposed model: Anchored rating = 4.53 + 1.215/THWLeftLeadLead + 0.001/THWLeftFollow + 3.069/THWLeadLead + 0.524/THWLead + 0.240/(TTCLead×TTCLeadLateral) + 30.487/(TTCLeftLead×TTCLeftLeadLateral) (Rrating2=0.54); Occlusion% = 0.381 + 0.150/THWLeftLeadLead ˗ 0.117/THWLeadLead + 0.021/THWLead + 2.648/(TTCLeftLead×TTCLeftLeadLateral) (Rocclusion2=0.58). In addition, it was shown that the GOMS model accounted for the observed differences of workload ratings from the empirical data (R2>0.83). In contrast to most previous studies that focus on average long-term traffic statistics (e.g., vehicles/lane/hour), this dissertation provided equations to predict two measures of workload using real-time traffic. The comparisons among three workload measures provided insights into how to… Advisors/Committee Members: Green, Paul A (committee member), Liu, Yili (committee member), Gonzalez, Richard D (committee member), Sarter, Nadine Barbara (committee member), Yang, Xi (Jessie) (committee member).

Subjects/Keywords: Human factors; Driver workload; Workload modeling; Transportation; Driver behavior; Driving; Industrial and Operations Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, K. (2019). Measuring and Quantifying Driver Workload on Limited Access Roads. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151422

Chicago Manual of Style (16^th Edition):

Liu, Ke. “Measuring and Quantifying Driver Workload on Limited Access Roads.” 2019. Doctoral Dissertation, University of Michigan. Accessed March 07, 2021. http://hdl.handle.net/2027.42/151422.

MLA Handbook (7^th Edition):

Liu, Ke. “Measuring and Quantifying Driver Workload on Limited Access Roads.” 2019. Web. 07 Mar 2021.

Vancouver:

Liu K. Measuring and Quantifying Driver Workload on Limited Access Roads. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2027.42/151422.

Council of Science Editors:

Liu K. Measuring and Quantifying Driver Workload on Limited Access Roads. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151422

Delft University of Technology

2. Pizzigoni, Edoardo (author). ACC target performance setting via NDS big data analysis.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff

► Advanced Driving Assistance Systems (ADAS) technologies like Adaptive Cruise Control (ACC) are becoming the normality for many users, and many major car manufacturers are introducing… (more)

▼ Advanced Driving Assistance Systems (ADAS) technologies like Adaptive Cruise Control (ACC) are becoming the normality for many users, and many major car manufacturers are introducing SAE level 2 and 3 automation systems into the market. The main advantage of Automated Vehicles (AV) will be the significant decrease in road accidents and casualties. However, a significant shift from conventional to automated vehicles must occur before it can have a positive impact on society. If the behaviour of the vehicle is not perceived as natural, the user will most likely not activate the ADAS features again. During this study a naturalistic dataset is used to investigate the driver behaviour, in the hope of bringing the current ACC logic to a more human-like behaviour that will feel more natural to the driver. The research question summarizes the final objective of this study: How can Naturalistic Driving Study (NDS) datasets be used in target performance setting for ACC systems? This study will answer the research question by studying human behaviour in the scene of following an accelerating vehicle. The main body of this thesis is divided in three chapters, one for each step of the research. First the information about the used datasets are provided together with the methodologies used to extract the relevant time-series data. Secondly driver behaviour models are created in order to mathematically characterize human behaviour. The strength of the created models is their ability to represent the full range of driver behaviour in terms of driving style. The aggressiveness parameter of the model can be easily adjusted to represent different percentiles of driver behaviour. This allows for a quick and effective tuning process: by changing a single parameter the driving style of the model can be fully modified. Finally, the driver behaviour models are implemented into a simulation environment. The models are simulated against an existing ACC logic in order to assess the difference in behaviour. The comparison highlighted two conclusions: first, the ACC logic behaves in a very conservative way compared to driver behaviour, especially when starting from standstill. Secondly, the kept by the ACC logic was not consistent throughout the speed range. This variation of the logic's driving style could result even more bothersome to the customer than its general conservative behaviour. The string stability of the driver behaviour models was also assessed. Although the proposed logic proved more stable than the regular ACC logic, it still cannot reach full string stability. Hopefully, with the method developed in this study, the process of getting accustomed to this new technology will become easier for the customer. Thanks to the driver behaviour models the motion of the vehicle can feel familiar and predictable, with the controller becoming part of the Human Machine Interface (HMI). As the customer gets more familiar with this technology his expectation will also increase and change, especially as the levels of automation start to… Advisors/Committee Members: Happee, Riender (mentor), Wang, Meng (graduation committee), Stapel, Jork (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: ACC; ADAS; driver modeling; driver behaviour; Automated vehicles; car following; naturalistic driving study

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pizzigoni, E. (. (2019). ACC target performance setting via NDS big data analysis. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff

Chicago Manual of Style (16^th Edition):

Pizzigoni, Edoardo (author). “ACC target performance setting via NDS big data analysis.” 2019. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff.

MLA Handbook (7^th Edition):

Pizzigoni, Edoardo (author). “ACC target performance setting via NDS big data analysis.” 2019. Web. 07 Mar 2021.

Vancouver:

Pizzigoni E(. ACC target performance setting via NDS big data analysis. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff.

Council of Science Editors:

Pizzigoni E(. ACC target performance setting via NDS big data analysis. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff

University of California – Berkeley

3. Lin, Theresa. Modeling of Human Decision Making via Direct and Optimization-based Methods for Semi-Autonomous Systems.

Degree: Mechanical Engineering, 2015, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/8303b28t

► In the generation where progression in technology have propelled the research of human-machine intelligent systems, it is becoming increasingly important to study the fundamental principles… (more)

▼ In the generation where progression in technology have propelled the research of human-machine intelligent systems, it is becoming increasingly important to study the fundamental principles behind human behaviors from a computational point of view. This thesis aims to use advanced technologies, combined with advanced modeling methodologies and modern control algorithms, to study the principles behind the modeling of human decision making for two purposes. First, to use computational modeling frameworks to better understand the mechanisms and factors which affect decision making in different problem contexts, both from the controls design and psychology perspective. Second, to introduce a unifying framework for integrating human policies into controller design in order to improve the performance of human-machine intelligent systems. Models are grouped into either the direct method or the optimization-based method. Direct methods map observations to decisions directly through stored function maps and are associated with lower-level, reflexive and repetitive behaviors. Optimization-based methods, associated with higher-level planning behaviors, require extra cognitive effort to generate state predictions in search for a solution that satisfies a set of criterions.To support the proposed modeling frameworks, both game-based and real-world experiments are conducted with the aid of advanced test apparatus and sensor technologies. Driving experiments on real roads and simulators explored driver behavior in everyday driving on the highway, extreme driving on slippery surfaces, and distracted driving with obstacles. Game-based experiments involving a projectile and a dual-task game were performed to collect consistent data in a controlled setting, and also designed to parallel the driving contexts in the real-world.Results from the experiments showed that in extreme driving, a piecewise-affine switched model with two modes was used to differentiate the behavior in the linear and saturation region of the tire. Simulations from a model predictive approach also showed that drivers need to be aware of the nonlinearity in the tire dynamics in order to follow a learned reference trajectory. Similarly, results from the projectile game also revealed that subjects adopted switched strategies due to the nonlinearity and uncertainties involved in the problem. In particular, subjects used switched strategies depending on whether it was an old or new scenarios. In the old scenario, subjects likely used a linear feedback strategy mapping errors directly to the change in control input. In the new scenario, subjects used an optimization-based strategy which minimized a combination of time to hit target and change in the control inputs in order to minimize the effect of uncertainty on the state trajectories.To validate the conjecture that humans perform mental simulations of states for the optimization-based algorithms, eye-tracking glasses were used to better estimate the cognitive states of the subjects. Eye-tracking on the driver during…

Subjects/Keywords: Cognitive psychology; Mechanical engineering; Controls; Driver Modeling; Human Modeling; Semiautonomous system

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APA (6^th Edition):

Lin, T. (2015). Modeling of Human Decision Making via Direct and Optimization-based Methods for Semi-Autonomous Systems. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/8303b28t

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lin, Theresa. “Modeling of Human Decision Making via Direct and Optimization-based Methods for Semi-Autonomous Systems.” 2015. Thesis, University of California – Berkeley. Accessed March 07, 2021. http://www.escholarship.org/uc/item/8303b28t.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lin, Theresa. “Modeling of Human Decision Making via Direct and Optimization-based Methods for Semi-Autonomous Systems.” 2015. Web. 07 Mar 2021.

Vancouver:

Lin T. Modeling of Human Decision Making via Direct and Optimization-based Methods for Semi-Autonomous Systems. [Internet] [Thesis]. University of California – Berkeley; 2015. [cited 2021 Mar 07]. Available from: http://www.escholarship.org/uc/item/8303b28t.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin T. Modeling of Human Decision Making via Direct and Optimization-based Methods for Semi-Autonomous Systems. [Thesis]. University of California – Berkeley; 2015. Available from: http://www.escholarship.org/uc/item/8303b28t

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

4. Gruppelaar, Virgílio (author). Measuring and modeling the role of time margins on drivers’ pedal control inputs during cornering speed adaptation.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:b935ec4d-fd54-4264-af5b-0f78f388b236

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Reducing conflicts between drivers and assistance systems has become an important issue in recent times, resulting in a need for a better understanding of how… (more)

Subjects/Keywords: driver modeling; time margins; human factors; speed control

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APA (6^th Edition):

Gruppelaar, V. (. (2017). Measuring and modeling the role of time margins on drivers’ pedal control inputs during cornering speed adaptation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b935ec4d-fd54-4264-af5b-0f78f388b236

Chicago Manual of Style (16^th Edition):

Gruppelaar, Virgílio (author). “Measuring and modeling the role of time margins on drivers’ pedal control inputs during cornering speed adaptation.” 2017. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:b935ec4d-fd54-4264-af5b-0f78f388b236.

MLA Handbook (7^th Edition):

Gruppelaar, Virgílio (author). “Measuring and modeling the role of time margins on drivers’ pedal control inputs during cornering speed adaptation.” 2017. Web. 07 Mar 2021.

Vancouver:

Gruppelaar V(. Measuring and modeling the role of time margins on drivers’ pedal control inputs during cornering speed adaptation. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:b935ec4d-fd54-4264-af5b-0f78f388b236.

Council of Science Editors:

Gruppelaar V(. Measuring and modeling the role of time margins on drivers’ pedal control inputs during cornering speed adaptation. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:b935ec4d-fd54-4264-af5b-0f78f388b236

Delft University of Technology

5. Droogendijk, C.G.I. (author). A new neuromuscular driver model for steering system development.

Degree: 2010, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:860c34b8-bb10-4ca2-94d3-33f70df55267

►

The evaluation of steering systems can be enhanced by replacing the test driver by a driver model capable of mimicking the driver’s needs, wants and… (more)

Subjects/Keywords: driver modeling; neuromuscular; steering system

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APA (6^th Edition):

Droogendijk, C. G. I. (. (2010). A new neuromuscular driver model for steering system development. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:860c34b8-bb10-4ca2-94d3-33f70df55267

Chicago Manual of Style (16^th Edition):

Droogendijk, C G I (author). “A new neuromuscular driver model for steering system development.” 2010. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:860c34b8-bb10-4ca2-94d3-33f70df55267.

MLA Handbook (7^th Edition):

Droogendijk, C G I (author). “A new neuromuscular driver model for steering system development.” 2010. Web. 07 Mar 2021.

Vancouver:

Droogendijk CGI(. A new neuromuscular driver model for steering system development. [Internet] [Masters thesis]. Delft University of Technology; 2010. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:860c34b8-bb10-4ca2-94d3-33f70df55267.

Council of Science Editors:

Droogendijk CGI(. A new neuromuscular driver model for steering system development. [Masters Thesis]. Delft University of Technology; 2010. Available from: http://resolver.tudelft.nl/uuid:860c34b8-bb10-4ca2-94d3-33f70df55267

Clemson University

6. Anderson, Jeffery Ryan. A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers.

Degree: PhD, Automotive Engineering, 2018, Clemson University

URL: https://tigerprints.clemson.edu/all_dissertations/2553

► In the study of vehicle dynamics and controls, modeling ultra-high performance maneuvers (i.e., minimum-time vehicle maneuvering) is a fascinating problem that explores the boundaries… (more)

▼ In the study of vehicle dynamics and controls, modeling ultra-high performance maneuvers (i.e., minimum-time vehicle maneuvering) is a fascinating problem that explores the boundaries of capabilities for a human controlling a machine. Professional human drivers are still considered the benchmark for controlling a vehicle during these limit handling maneuvers. Different drivers possess unique driving styles, i.e. preferences and tendencies in their local decisions and corresponding inputs to the vehicle. These differences in the driving style among professional drivers or sets of drivers are duly considered in the vehicle development process for component selection and system tuning to push the limits of achievable lap times. This work aims to provide a mathematical framework for modeling driving styles of professional drivers that can then be embedded in the vehicle design and development process. This research is conducted in three separate phases. The first part of this work introduces a cascaded optimization structure that is capable of modeling driving style. Model Predictive Control (MPC) provides a natural framework for modeling the human decision process. In this work, the inner loop of the cascaded structure uses an MPC receding horizon control strategy which is tasked with finding the optimal control inputs (steering, brake, throttle, etc.) over each horizon while minimizing a local cost function. Therein, we extend the typical fixed-cost function to be a blended cost capable of optimizing different objectives. Then, an outer loop finds the objective weights used in each MPC control horizon. It is shown that by varying the driver's objective between key horizons, some of the sub-optimality inherent to the MPC process can be alleviated. In the second phase of this work, we explore existing onboard measurements of professional drivers to compare different driving styles. We outline a novel racing line reconstruction technique rooted in optimal control theory to reconstruct the driving lines for different drivers from a limited set of measurements. It is demonstrated that different drivers can achieve nearly identical lap times while adopting different racing lines. In the final phase of this work, we use our racing line technique and our cascaded optimization framework to fit computable models for different drivers. For this, the outer loop of the cascaded optimization finds the set of objective weights used in each local MPC horizon that best matches simulation to onboard measurements. These driver models will then be used to optimize vehicle design parameters to suit each driving style. It will be shown that different driving styles will yield different parameters that optimize the driver/vehicle system. Advisors/Committee Members: Beshah Ayalew, Timothy Rhyne, Ardalan Vahidi, Robert Prucka.

Subjects/Keywords: Driver Modeling; Minimum-Time Vehicle Maneuvering; Optimal Control; Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Anderson, J. R. (2018). A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2553

Chicago Manual of Style (16^th Edition):

Anderson, Jeffery Ryan. “A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers.” 2018. Doctoral Dissertation, Clemson University. Accessed March 07, 2021. https://tigerprints.clemson.edu/all_dissertations/2553.

MLA Handbook (7^th Edition):

Anderson, Jeffery Ryan. “A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers.” 2018. Web. 07 Mar 2021.

Vancouver:

Anderson JR. A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers. [Internet] [Doctoral dissertation]. Clemson University; 2018. [cited 2021 Mar 07]. Available from: https://tigerprints.clemson.edu/all_dissertations/2553.

Council of Science Editors:

Anderson JR. A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers. [Doctoral Dissertation]. Clemson University; 2018. Available from: https://tigerprints.clemson.edu/all_dissertations/2553

7. Waters, Thomas Robert. Adaptive driver modeling using machine learning algorithms for the energy optimal planning of velocity trajectories for electric vehicles and realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds.

Degree: MS, Mechanical Engineering, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/59310

► Part 1 Driver assistance systems show the potential to increase the fuel economy and optimize the range of standard and electric vehicles. Eco-driving focused systems… (more)

▼ Part 1 Driver assistance systems show the potential to increase the fuel economy and optimize the range of standard and electric vehicles. Eco-driving focused systems optimize velocity trajectories with respect to energy consumption and suggest these optimized speeds to drivers with the goal of reducing overall energy consumption. Because the systems have no direct control over vehicle behavior, the driver’s inclination to follow the commands is important to their effectiveness. This can be improved by personalizing the velocity commands to suit an individual’s driving behavior, requiring a model capable of accurately predicting styles of individual drivers. Two methods for identifying, modeling, and predicting driver behavior using driving data time-series are investigated. The first, pattern recognition-based approach breaks down the data into homogeneous segments using heuristic, dynamic programming, and bottom-up methods. Segments are grouped based on acceleration behavior and used, in conjunction with function-fit regression and system identification methods, to construct models describing driving behavior. Contrary to the first approach, the second, machine learning based method constructs a model using an entire time-series by analyzing relationships between multiple variables. Finally, each method is evaluated in it’s ability to accurately predict driver acceleration and velocity behavior. Part 2 Enforcing multiple, sometimes conflicting control objectives is a challenge present in modern advanced driver assistance systems. Drivers are capable of activating multiple modules simultaneously where safety must be guaranteed at all times. Examples includes adaptive speed regulation, where the vehicle must achieve a desired speed while maintaining a safe distance to any preceding vehicle, and lane keeping, where a vehicle is kept safely within the bounds of a lane. Provably safe algorithms for both adaptive speed regulation and lane keeping are introduced and used to run experiments on two robotic testbeds. The underlying algorithms are based on control Lyapunov functions for performance, a control barrier functions for safety, and a real-time quadratic program for mediating the conflicting demands between the two. The Robotarium, a robotic testbed that allows students, as well as researchers less experienced with hardware, to experiment with advanced control concepts in a safe and standardized environment, is compared with a more expensive OptiTrack based Khepera robot testbed. Advisors/Committee Members: Ames, Aaron (advisor), Rogers, Jonathon (advisor), Sawodny, Oliver (advisor), Wohlhaupter, Uli (advisor).

Subjects/Keywords: Driver modeling; Nonlinear control

…Velocity Trajectory Modeling . . . . . . . . . . . . . . . . . . 51 Evaluation of Driver… …individual drivers. Two methods for identifying, modeling, and predicting driver behavior using… …work, we explore adaptive driver behavior modeling methods, including pattern recognition and… …such as driver assistance systems and driver behavior modeling are introduced. 1.1 Driver… …x5B;2], advanced driver modeling also has applications in driving training and coaching…

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APA (6^th Edition):

Waters, T. R. (2018). Adaptive driver modeling using machine learning algorithms for the energy optimal planning of velocity trajectories for electric vehicles and realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59310

Chicago Manual of Style (16^th Edition):

Waters, Thomas Robert. “Adaptive driver modeling using machine learning algorithms for the energy optimal planning of velocity trajectories for electric vehicles and realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds.” 2018. Masters Thesis, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/59310.

MLA Handbook (7^th Edition):

Waters, Thomas Robert. “Adaptive driver modeling using machine learning algorithms for the energy optimal planning of velocity trajectories for electric vehicles and realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds.” 2018. Web. 07 Mar 2021.

Vancouver:

Waters TR. Adaptive driver modeling using machine learning algorithms for the energy optimal planning of velocity trajectories for electric vehicles and realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/59310.

Council of Science Editors:

Waters TR. Adaptive driver modeling using machine learning algorithms for the energy optimal planning of velocity trajectories for electric vehicles and realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59310

8. Abrashov, Sergey. Étude et modélisation du conducteur pour la conception de systèmes d’assistance à la conduite : Driver study and modeling for driving assistance systems developement.

Degree: Docteur es, Automatique, productique, signal et image, ingénierie cognitique, 2017, Bordeaux

URL: http://www.theses.fr/2017BORD0558

►

Le confort et la sécurité de conduite sont les principaux critères de vente de l’industrie automobile actuelle. De nombreux projets de recherche sont mis en… (more)

▼

Le confort et la sécurité de conduite sont les principaux critères de vente de l’industrie automobile actuelle. De nombreux projets de recherche sont mis en place afin de les améliorer et pour faire face aux mesures de législation et de contrôle mises en place pour réduire le nombre d’accidents routiers. Les mesures semblent efficaces : en France,par exemple, le nombre des accidents mortels diminue de 11% en moyenne chaque année.D’après de récentes études, 90% de ces accidents ont pour cause le facteur humain et il devient nécessaire de prendre en compte le conducteur pendant la phase de conception des systèmes de sécurité et d’aide à la conduite. Une assistance à la conduite basée sur le partage du contrôle du véhicule entre le conducteur et l’automate est un des axes de recherche privilégiés de l’industrie, notamment pour améliorer la sécurité.Il est maintenant devenu possible de récupérer une très grande quantité d’information sur l’environnement et de réaliser une interaction intelligente entre les différents acteurs du trafic. Les techniques existantes permettent même la conduite partagée entre le véhicule et le conducteur et, dans un horizon plus lointain, d’envisager un véhicule complètement autonome. Dans les situations de conduite automatisée, un algorithme adéquat est nécessaire pour remplacer le conducteur.L’intérêt principal de cette recherche se situe au niveau de l’interaction entre le conducteur et l’algorithme d’assistance ou de conduite automatisée. Il est indispensable de connaître et de comprendre le comportement du conducteur dans sa façon de conduire,de contrôler le véhicule et de prendre une décision. Par conséquent, un modèle adapté aux besoins est nécessaire. En plus de la nécessité de disposer d’un modèle suffisamment riche pour décrire le comportement de différents conducteurs dans les situations routières les plus fréquentes, il est indispensable de disposer d’une méthode de synthèse des systèmes d’assistance sur la base de ces modèles.

Driving comfort and safety are the main points of interest for the automotive industry. Many research projects were realized in order to improve them and to reduce the number of road accidents. The measures seem to be effective : in France, for example, the number of fatal accidents decreases by 11% on average each year. According to recent studies, 90% of these accidents are caused by the human factor. As a consequence, it becomes necessary to take the driver into account during the design of driving assistance systems. An assistance based on the control sharing between the driver and the automatic pilot is one of the main topics of research and a way to improve safety. It has now become possible to recover a very large amount of information on the environment and to achieve intelligent interaction between the various actors in the traffic. Existing technologies even allow imagining a completely autonomous driving in a more distant horizon. In such a situation, an adequate algorithm is required to replace the human driver.The main interest of this…

Advisors/Committee Members: Malti, Rachid (thesis director), Moreau, Xavier (thesis director).

Subjects/Keywords: Modélisation du conducteur; ADAS; Planification d’expérience; Driver modeling; ADAS; Experiment planning

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APA (6^th Edition):

Abrashov, S. (2017). Étude et modélisation du conducteur pour la conception de systèmes d’assistance à la conduite : Driver study and modeling for driving assistance systems developement. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2017BORD0558

Chicago Manual of Style (16^th Edition):

Abrashov, Sergey. “Étude et modélisation du conducteur pour la conception de systèmes d’assistance à la conduite : Driver study and modeling for driving assistance systems developement.” 2017. Doctoral Dissertation, Bordeaux. Accessed March 07, 2021. http://www.theses.fr/2017BORD0558.

MLA Handbook (7^th Edition):

Abrashov, Sergey. “Étude et modélisation du conducteur pour la conception de systèmes d’assistance à la conduite : Driver study and modeling for driving assistance systems developement.” 2017. Web. 07 Mar 2021.

Vancouver:

Abrashov S. Étude et modélisation du conducteur pour la conception de systèmes d’assistance à la conduite : Driver study and modeling for driving assistance systems developement. [Internet] [Doctoral dissertation]. Bordeaux; 2017. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2017BORD0558.

Council of Science Editors:

Abrashov S. Étude et modélisation du conducteur pour la conception de systèmes d’assistance à la conduite : Driver study and modeling for driving assistance systems developement. [Doctoral Dissertation]. Bordeaux; 2017. Available from: http://www.theses.fr/2017BORD0558

The Ohio State University

9. Schnelle, Scott C. Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-Vehicle Interactive Control.

Degree: PhD, Mechanical Engineering, 2016, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1480362246357462

► Advanced driver assistance systems (ADAS) are a subject of increasing interest as they are being implemented on production vehicles and also continue to be developed… (more)

Subjects/Keywords: Mechanical Engineering; Driver Modeling, ADAS, Human-Vehicle Interaction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Schnelle, S. C. (2016). Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-Vehicle Interactive Control. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1480362246357462

Chicago Manual of Style (16^th Edition):

Schnelle, Scott C. “Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-Vehicle Interactive Control.” 2016. Doctoral Dissertation, The Ohio State University. Accessed March 07, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480362246357462.

MLA Handbook (7^th Edition):

Schnelle, Scott C. “Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-Vehicle Interactive Control.” 2016. Web. 07 Mar 2021.

Vancouver:

Schnelle SC. Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-Vehicle Interactive Control. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2021 Mar 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1480362246357462.

Council of Science Editors:

Schnelle SC. Development of Personalized Lateral and Longitudinal Driver Behavior Models for Optimal Human-Vehicle Interactive Control. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1480362246357462

Virginia Tech

10. D'Angio, Paul Christopher. Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®.

Degree: PhD, Mechanical Engineering, 2012, Virginia Tech

URL: http://hdl.handle.net/10919/27582

► This work proposes a series of driver assistance technologies that enable blind persons to safely and independently operate an automobile on standard public roads. Such… (more)

▼ This work proposes a series of driver assistance technologies that enable blind persons to safely and independently operate an automobile on standard public roads. Such technology could additionally benefit sighted drivers by augmenting vision with suggestive cues during normal and low-visibility driving conditions. This work presents a non-visual human-computer interface system with passive and adaptive controlling software to realize this type of driver assistance technology. The research and development behind this work was made possible through the Blind Driver Challenge® initiative taken by the National Federation of the Blind. The instructional technologies proposed in this work enable blind drivers to operate an automobile through the provision of steering wheel angle and speed cues to the driver in a non-visual method. This paradigm imposes four principal functionality requirements: Perception, Motion Planning, Reference Transformations, and Communication. The Reference Transformation and Communication requirements are the focus of this work and convert motion planning trajectories into a series of non-visual stimuli that can be communicated to the human driver. This work proposes two separate algorithms to perform the necessary reference transformations described above. The first algorithm, called the Passive Non-Visual Interface Driver, converts the planned trajectory data into a form that can be understood and reliably interacted with by the blind driver. This passive algorithm performs the transformations through a method that is independent of the driver. The second algorithm, called the Adaptive Non-Visual Interface Driver, performs similar trajectory data conversions through methods that adapt to each particular driver. This algorithm uses Model Predictive Control supplemented with Artificial Neural Network driver models to generate non-visual stimuli that are predicted to induce optimal performance from the driver. The driver models are trained online and in real-time with a rapid training approach to continually adapt to changes in the driver's dynamics over time. The communication of calculated non-visual stimuli is subsequently performed through a Non-Visual Interface System proposed by this work. This system is comprised of two non-visual human computer interfaces that communicate driving information through haptic stimuli. The DriveGrip interface is pair of vibro-tactile gloves that communicate steering information through the driverâ s hands and fingers. The SpeedStrip interface is a vibro-tactile cushion fitted on the driverâ s seat that communicates speed information through the driver's legs and back. The two interfaces work simultaneously to provide a continuous stream of directions to the driver as he or she navigates the vehicle. Advisors/Committee Members: Cooper, Robin K. Panneton (committee member), Sturges, Robert H. (committee member), Southward, Steve C. (committee member), Leonessa, Alexander (committeecochair), Hong, Dennis W. (committeecochair).

Subjects/Keywords: Real-Time Neural Network Driver Modeling; Driver Assistive Technologies; Model Predictive Control; Quazi-Newton Optimization; Non-Visual Human Computer Interfaces

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

D'Angio, P. C. (2012). Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/27582

Chicago Manual of Style (16^th Edition):

D'Angio, Paul Christopher. “Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®.” 2012. Doctoral Dissertation, Virginia Tech. Accessed March 07, 2021. http://hdl.handle.net/10919/27582.

MLA Handbook (7^th Edition):

D'Angio, Paul Christopher. “Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®.” 2012. Web. 07 Mar 2021.

Vancouver:

D'Angio PC. Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10919/27582.

Council of Science Editors:

D'Angio PC. Adaptive and Passive Non-Visual Driver Assistance Technologies for the Blind Driver Challenge®. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/27582

University of California – San Diego

11. Martin, Sujitha Catherine. Vision based, Multi-cue Driver Models for Intelligent Vehicles.

Degree: Electrical Engineering (Intelsys, Robotics and Cont), 2016, University of California – San Diego

URL: http://www.escholarship.org/uc/item/4v27v981

► This dissertation seeks to enable intelligent vehicles to see, to predict intentions, to understand and to model the state of driver.We developed a state of… (more)

▼ This dissertation seeks to enable intelligent vehicles to see, to predict intentions, to understand and to model the state of driver.We developed a state of the art vision based non-contact gaze estimation framework by carefully designing submodules which will build up to achieve continuous and robust estimation. Key modules in this system include, face detection using deep convolutional neural networks, landmark estimation from cascaded regression models, head pose from geometrical correspondence mapping from 2-D points in the image plane to 3-D points in the head model, horizontal gaze surrogate based on geometrical formulation of the eye ball and iris position, vertical gaze surrogate based on openness of the upper eye lids and appearance descriptor, and finally, a 9-class gaze zone estimation from naturalistic driving data driven random forest algorithm. We developed a framework to model driver's gaze behavior by representing the scanpath over a time period using glance durations and transition frequencies. As a use case, we explore the driver's scanpath patterns during maneuvers executed in freeway driving, namely, left lane change maneuver, right lane change maneuver and lane keep. It is shown that condensing temporal scanpath into glance durations and glance transition frequencies leads to recurring patterns based on driver activities. Furthermore, modeling these patterns show predictive powers in maneuver detection up to a few seconds a priori and show a promise for developing gaze guidance during take over requests in highly automated vehicles.We introduce a framework to model the spatio-temporal movements of head, eyes and hands given naturalistic driving data of looking-in at the driver for any events or tasks performed of interest. As a use case, we explore the temporal coordination of the modalities on data of drivers executing maneuvers at stop-controlled intersections; the maneuvers executed are go straight, turn left and turn right. In sequentially increasing time windows, by training classifiers which have the ability to provide discriminative quality of its input variable, the experimental study at intersections shows which type of, when and how long distinguishable preparatory movements occur in the range of a few milliseconds to a few seconds.We introduce one part of the Vision for Intelligent Vehicles and Applications (VIVA) challenge, namely, the VIVA-face challenge. VIVA is a platform designed to share naturalistic driving data with the community in order to: present issues and challenges in vision from real-world driving conditions, benchmark existing vision approaches using proper metrics and progress the development of future vision algorithms. With a special focus on challenges from looking inside at the driver’s face, we provide information on how the data is acquired and annotated, and how methods are benchmarked, compared and shared on leaderboards.Finally, we propose de-identification filters for protecting the privacy of drivers while preserving sufficient details to infer driver…

Subjects/Keywords: Engineering; Driver state modeling; Gaze estimation; Intelligent vehicles; Multi-cue fusion; Naturalistic driving dataset; Privacy

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APA (6^th Edition):

Martin, S. C. (2016). Vision based, Multi-cue Driver Models for Intelligent Vehicles. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/4v27v981

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Martin, Sujitha Catherine. “Vision based, Multi-cue Driver Models for Intelligent Vehicles.” 2016. Thesis, University of California – San Diego. Accessed March 07, 2021. http://www.escholarship.org/uc/item/4v27v981.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Martin, Sujitha Catherine. “Vision based, Multi-cue Driver Models for Intelligent Vehicles.” 2016. Web. 07 Mar 2021.

Vancouver:

Martin SC. Vision based, Multi-cue Driver Models for Intelligent Vehicles. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2021 Mar 07]. Available from: http://www.escholarship.org/uc/item/4v27v981.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Martin SC. Vision based, Multi-cue Driver Models for Intelligent Vehicles. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/4v27v981

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Toronto

12. Zhang, Wei Jia Jia. A Smart Gate Driver IC for Enhancement Mode GaN Power Transistor with Dead-time Correction.

Degree: PhD, 2019, University of Toronto

URL: http://hdl.handle.net/1807/97759

► Researchers in power electronics have been optimizing silicon devices with novel structures and gate drivers to keep up with the growing demands for power management.… (more)

▼ Researchers in power electronics have been optimizing silicon devices with novel structures and gate drivers to keep up with the growing demands for power management. However, the improvement has slowed down as silicon power devices approach their theoretical limits. The emergence of GaN power devices provides an avenue to meet the continuing demands for performance improvement. GaN demonstrates material properties such as wider bandgap, higher electron mobility, and higher critical electrical field when compared to silicon. In particular, the AlGaN/GaN HEMTs exhibit great potential of lower on-resistance and smaller gate capacitance, leading to reduced conduction and switching losses. Their fast switching frequency (>10’s MHz) can further help to reduce the size of the passive components. Driving enhancement mode (E-mode) GaN HEMT is similar to driving silicon power MOSFETs but with additional challenges. The optimum gate driving voltage for most E-mode HEMTs is 6 V but the maximum allowed gate voltage is 7 V, leaving a narrow driving window. This restricts the maximum dν/dt and di/dt as little gate ringing can be tolerated. Consequentially, the active driving technique is often employed to suppress the gate ringing while maintaining the fast turn-on and turn-off transitions. Moreover, the fast switching requires precise dead-time control to avoid shoot-through current between the supply voltage and ground. However, excessively long dead-times lead to unwanted reverse conduction and additional power loss. Therefore, high precision detection circuits are important for dead-time correction as the load current changes. In this thesis, a reverse conduction sensing circuit that can accommodate the large voltage swings at the switching node is designed to track the presence of reverse conduction. The proposed segmented gate driver IC for E-mode GaN power output stages, fabricated using TSMC’s 0.18 µm Gen-2 process, is equipped with dead-time correction for improving system efficiency and highly configurable gate drivers for ringing suppression. The one-step correction mode can optimize the dead-times for both the turn-on and turn-off edges within one switching cycle for switching frequencies of up to 10 MHz with 0.32 ns precision. This allows the power converters to maintain optimal conversion efficiency over a wide output current range. Advisors/Committee Members: Ng, Wai Tung, Electrical and Computer Engineering.

Subjects/Keywords: Dead-time control; Device Modeling; GaN HEMT; Gate driver IC; Segmented drivers; SenseFET Clamping; 0537

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APA (6^th Edition):

Zhang, W. J. J. (2019). A Smart Gate Driver IC for Enhancement Mode GaN Power Transistor with Dead-time Correction. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/97759

Chicago Manual of Style (16^th Edition):

Zhang, Wei Jia Jia. “A Smart Gate Driver IC for Enhancement Mode GaN Power Transistor with Dead-time Correction.” 2019. Doctoral Dissertation, University of Toronto. Accessed March 07, 2021. http://hdl.handle.net/1807/97759.

MLA Handbook (7^th Edition):

Zhang, Wei Jia Jia. “A Smart Gate Driver IC for Enhancement Mode GaN Power Transistor with Dead-time Correction.” 2019. Web. 07 Mar 2021.

Vancouver:

Zhang WJJ. A Smart Gate Driver IC for Enhancement Mode GaN Power Transistor with Dead-time Correction. [Internet] [Doctoral dissertation]. University of Toronto; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1807/97759.

Council of Science Editors:

Zhang WJJ. A Smart Gate Driver IC for Enhancement Mode GaN Power Transistor with Dead-time Correction. [Doctoral Dissertation]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/97759

University of Waterloo

13. Khosravani, Saeid. Vehicle Stability Control Considering the Driver-in-the-Loop.

Degree: 2016, University of Waterloo

URL: http://hdl.handle.net/10012/10610

► A driver‐in‐the‐loop modeling framework is essential for a full analysis of vehicle stability systems. In theory, knowing the vehicle’s desired path (driver’s intention), the problem… (more)

▼ A driver‐in‐the‐loop modeling framework is essential for a full analysis of vehicle stability systems. In theory, knowing the vehicle’s desired path (driver’s intention), the problem is reduced to a standard control system in which one can use different methods to produce a (sub) optimal solution. In practice, however, estimation of a driver’s desired path is a challenging – if not impossible – task. In this thesis, a new formulation of the problem that integrates the driver and the vehicle model is proposed to improve vehicle performance without using additional information from the future intention of the driver. The driver’s handling technique is modeled as a general function of the road preview information as well as the dynamic states of the vehicle. In order to cover a variety of driving styles, the time‐ varying cumulative driver's delay and model uncertainties are included in the formulation. Given that for practical implementations, the driver’s future road preview data is not accessible, this information is modeled as bounded uncertainties. Subsequently, a state feedback controller is designed to counteract the negative effects of a driver’s lag while makes the system robust to modeling and process uncertainties. The vehicle’s performance is improved by redesigning the controller to consider a parameter varying model of the driver‐vehicle system. An LPV controller robust to unknown time‐varying delay is designed and the disturbance attenuation of the closed loop system is estimated. An approach is constructed to identify the time‐varying parameters of the driver model using past driving information. The obtained gains are clustered into several modes and the transition probability of switching between different driving‐styles (modes) is calculated. Based on this analysis, the driver‐vehicle system is modeled as a Markovian jump dynamical system. Moreover, a complementary analysis is performed on the convergence properties of the mode‐dependent controller and a tighter estimation for the maximum level of disturbance rejection of the LPV controller is obtained. In addition, the effect of a driver’s skills in controlling the vehicle while the tires are saturated is analyzed. A guideline for analysis of the nonlinear system performance with consideration to the driver’s skills is suggested. Nonlinear controller design techniques are employed to attenuate the undesirable effects of both model uncertainties and tire saturation.

Subjects/Keywords: Vehicle Stability; Driver Modeling; Delay; Robust Control; Markov Jump; Linear Parameter Varying Systems

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APA (6^th Edition):

Khosravani, S. (2016). Vehicle Stability Control Considering the Driver-in-the-Loop. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10610

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Khosravani, Saeid. “Vehicle Stability Control Considering the Driver-in-the-Loop.” 2016. Thesis, University of Waterloo. Accessed March 07, 2021. http://hdl.handle.net/10012/10610.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Khosravani, Saeid. “Vehicle Stability Control Considering the Driver-in-the-Loop.” 2016. Web. 07 Mar 2021.

Vancouver:

Khosravani S. Vehicle Stability Control Considering the Driver-in-the-Loop. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10012/10610.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khosravani S. Vehicle Stability Control Considering the Driver-in-the-Loop. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10610

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Washington

14. Wu, Yuqing. Quantifying Drivers Foot Movements and Pedal Misapplication Errors.

Degree: PhD, 2016, University of Washington

URL: http://hdl.handle.net/1773/35229

► Pedal errors refer to the situation when the driver mistakenly presses the wrong pedal or does not press the pedal at all. A negative outcome… (more)

▼ Pedal errors refer to the situation when the driver mistakenly presses the wrong pedal or does not press the pedal at all. A negative outcome of pedal misapplications is a sudden acceleration event, which has been associated with crashes. However, there is currently little information on the specific contributions. The goal of this dissertation is to identify the factors associated with a higher likelihood of pedal errors through models of driver’s foot movements. Data from 87 unique participants were collected from three studies: a driving simulator, parking lot study, and naturalistic driving. There were different foot trajectories observed that could be classified as a direct hit, hesitation, corrected trajectory, or pedal error. Within the pedal errors, four different sub-categories were observed: wrong pedal, slip, miss, and both pedals. These errors (3.27%) were not as common as the other foot trajectories and were therefore placed into one group for further analysis. Using a repeated logit model, pedal errors were shown to be associated with age-related and situational factors, including the location of the triggered event. Further exploration of the driver-related differences in movements was conducted using a functional principal components analysis that showed that the largest contribution to pedal errors were observed early in the foot movement, when compared to the direct hit and corrected trajectory. Exploration of the situational context was further examined using a naturalistic study, which showed that turning maneuvers were less likely associated with errors as drivers had their foot on the brake pedal more often. The parking and start-up sequence also had an impact on the likelihood of a pedal error. Hence, a tight parking maneuver with a sudden event was also used. No pedal errors were observed in this study. But there were more braking events observed in the parking study when compared to a similar event in the simulator study. In summary, the series of study showed that an algorithm could be designed to detect a potential pedal error early in the foot movement process such that an alert could be provided to driver in a reasonable timeframe to allow correction of the movement. This dissertation describes the factors that can be considered for such an algorithm, and the process to identify these factors. Advisors/Committee Members: Boyle, Linda Ng (advisor).

Subjects/Keywords: Driver behavior; Foot trajectory; Pedal application types; Pedal misapplication errors; Statistical modeling; Industrial engineering; Transportation; Statistics; industrial engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wu, Y. (2016). Quantifying Drivers Foot Movements and Pedal Misapplication Errors. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/35229

Chicago Manual of Style (16^th Edition):

Wu, Yuqing. “Quantifying Drivers Foot Movements and Pedal Misapplication Errors.” 2016. Doctoral Dissertation, University of Washington. Accessed March 07, 2021. http://hdl.handle.net/1773/35229.

MLA Handbook (7^th Edition):

Wu, Yuqing. “Quantifying Drivers Foot Movements and Pedal Misapplication Errors.” 2016. Web. 07 Mar 2021.

Vancouver:

Wu Y. Quantifying Drivers Foot Movements and Pedal Misapplication Errors. [Internet] [Doctoral dissertation]. University of Washington; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1773/35229.

Council of Science Editors:

Wu Y. Quantifying Drivers Foot Movements and Pedal Misapplication Errors. [Doctoral Dissertation]. University of Washington; 2016. Available from: http://hdl.handle.net/1773/35229

Arizona State University

15. Jahagirdar, Tanvi. Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars.

Degree: Computer Science, 2015, Arizona State University

URL: http://repository.asu.edu/items/29885

► Driver distraction research has a long history spanning nearly 50 years, intensifying in the last decade. The focus has always been on identifying the distractive… (more)

Subjects/Keywords: Computer science; Computer engineering; Automotive engineering; cognitive load; driver distraction; human-car interaction; modeling; multi-modal; smart cars

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APA (6^th Edition):

Jahagirdar, T. (2015). Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/29885

Chicago Manual of Style (16^th Edition):

Jahagirdar, Tanvi. “Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars.” 2015. Masters Thesis, Arizona State University. Accessed March 07, 2021. http://repository.asu.edu/items/29885.

MLA Handbook (7^th Edition):

Jahagirdar, Tanvi. “Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars.” 2015. Web. 07 Mar 2021.

Vancouver:

Jahagirdar T. Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars. [Internet] [Masters thesis]. Arizona State University; 2015. [cited 2021 Mar 07]. Available from: http://repository.asu.edu/items/29885.

Council of Science Editors:

Jahagirdar T. Modeling and Measuring Cognitive Load to Reduce Driver Distraction in Smart Cars. [Masters Thesis]. Arizona State University; 2015. Available from: http://repository.asu.edu/items/29885

16. Sameera Chathuranga, Koththigoda Kankanamge. Crash analysis and road user survey to identify issues and countermeasures for older drivers in Kansas.

Degree: MS, Department of Civil Engineering, 2017, Kansas State University

URL: http://hdl.handle.net/2097/35452

► The percentage of the U.S. population aged 65 years or older is increasing rapidly. Statistics also show this age group was 14.9 percent of the… (more)

▼ The percentage of the U.S. population aged 65 years or older is increasing rapidly. Statistics also show this age group was 14.9 percent of the population in 2015 and is expected to be 20.7 to 21.4 percent for the years 2030–2050. Kansas has similar statewide trends with its aging population. Therefore, identifying issues, concerns, and factors associated with severity of older-driver crashes in Kansas is necessary. The Kansas Crash Analysis and Reporting System (KCARS) database maintained by Kansas Department of Transportation was used in this study to identify older-driver crash characteristics, compare older drivers with all drivers, and develop crash severity models. According to KCARS data, older drivers were involved in more than one in five fatal injuries out of all drivers in Kansas from 2010 to 2014. When compared with all drivers, older drivers were overly represented in fatal and incapacitating injuries. The percentage of older-driver fatal injuries was more than the twice that of all drivers. When compared with all drivers, older drivers were involved more often in crashes at four-way intersections, on straight and level roads, in daylight hours, and at a stop or yield signs. An in-depth crash severity analysis was carried out for the older drivers involved in crashes. Three separate binary logistic regression models were developed for single-vehicle crashes where only the older driver was present (Model A), single-vehicle crashes involving an older driver with at least one passenger (Model B), and multi-vehicle crashes involving at least one older driver (Model C). From the crash severity analysis, it was found that left turns were significant in changing the crash severity for Model A, but it was not significant in model B, meaning that older drivers may be safer with passengers. For Model B, none of the passenger attributes were significant, though it was originally developed to identify passenger attributes. Gender of the older driver was not significant in any model. For all models, variables such as safety equipment use, crash location, weather conditions, driver ejected or trapped, and light conditions distinguished crash severity. Furthermore, for Model A, variables such as day of the week, speed, accident class, and maneuver, distinguished crash severity. Moreover, accident class, surface type, and vehicle type changed crash severity in Model B. Number of vehicles, speed, collision type, maneuver, and two-lane roads were significant in Model C. A road-user survey was also conducted to identify habits, needs, and concerns of Kansas' aging road users since it was not advisable to conclude safety factors solely on crash data. The probability of occurrence was calculated by taking the weighted average of answers to a question. Then a contingency table analysis was carried out to identify relationships among variables. For older drivers, seatbelt use as a driver had the highest probability of occurrence. Driving in heavy traffic, merging into traffic, moving away from traffic, and judging gaps… Advisors/Committee Members: Sunanda Dissanayake.

Subjects/Keywords: Older driver safety; Crash severity modeling

…driver safety. Previous researchers have used different kinds of statistical modeling… …logistic regression modeling was chosen to model crash severity of older-driver-involved crashes… …driver-related issues in Kansas. 4 1.3 Objectives The main objective of this research was to… …achieved by summarizing general crash data, comparing older-driver crash characteristics with all… …and includes studies related to factors affecting older-driver crashes, statistical…

5 more images…

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APA (6^th Edition):

Sameera Chathuranga, K. K. (2017). Crash analysis and road user survey to identify issues and countermeasures for older drivers in Kansas. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/35452

Chicago Manual of Style (16^th Edition):

Sameera Chathuranga, Koththigoda Kankanamge. “Crash analysis and road user survey to identify issues and countermeasures for older drivers in Kansas.” 2017. Masters Thesis, Kansas State University. Accessed March 07, 2021. http://hdl.handle.net/2097/35452.

MLA Handbook (7^th Edition):

Sameera Chathuranga, Koththigoda Kankanamge. “Crash analysis and road user survey to identify issues and countermeasures for older drivers in Kansas.” 2017. Web. 07 Mar 2021.

Vancouver:

Sameera Chathuranga KK. Crash analysis and road user survey to identify issues and countermeasures for older drivers in Kansas. [Internet] [Masters thesis]. Kansas State University; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2097/35452.

Council of Science Editors:

Sameera Chathuranga KK. Crash analysis and road user survey to identify issues and countermeasures for older drivers in Kansas. [Masters Thesis]. Kansas State University; 2017. Available from: http://hdl.handle.net/2097/35452

Virginia Tech

17. Elhenawy, Mohammed Mamdouh Zakaria. Appling Machine and Statistical Learning Techniques to Intelligent Transport Systems: Bottleneck Identification and Prediction, Dynamic Travel Time Prediction, Driver Run-Stop Behavior Modeling, and Autonomous Vehicle Control at Intersections.

Degree: PhD, Computer Engineering, 2015, Virginia Tech

URL: http://hdl.handle.net/10919/73790

► In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify… (more)

▼ In this dissertation, new algorithms that address three traffic problems of major importance are developed. First automatic identification and prediction algorithms are developed to identify and predict the occurrence of traffic congestion. The identification algorithms concoct a model to identify speed thresholds by exploiting historical spatiotemporal speed matrices. We employ the speed model to define a cutoff speed separating free-flow from congested traffic. We further enhance our algorithm by utilizing weather and visibility data. To our knowledge, we are the first to include weather and visibility variables in formulating an automatic congestion identification model. We also approach the congestion prediction problem by adopting an algorithm which employs Adaptive Boosting machine learning classifiers again something novel that has not been done previously. The algorithm is promising where it resulted in a true positive rate slightly higher than 0.99 and false positive rate less than 0.001. We next address the issue of travel time modeling. We propose algorithms to model travel time using various machine learning and statistical learning techniques. We obtain travel time models by employing the historical spatiotemporal speed matrices in conjunction with our algorithms. The algorithms yield pertinent information regarding travel time reliability and prediction of travel times. Our proposed algorithms give better predictions compared to the state of practice algorithms. Finally we consider driver safety at signalized intersections and uncontrolled intersections in a connected vehicles environment. For signalized intersections, we exploit datasets collected from four controlled experiments to model the stop-run behavior of the driver at the onset of the yellow indicator for various roadway surface conditions and multiple vehicle types. We further propose a new variable (predictor) related to driver aggressiveness which we estimate by monitoring how drivers respond to yellow indications. The performance of the stop-run models shows improvements after adding the new aggressiveness predictor. The proposed models are practical and easy to implement in advanced driver assistance systems. For uncontrolled intersections, we present a game theory based algorithm that models the intersection as a chicken game to solve the conflicts between vehicles crossing the intersection. The simulation results show a 49% saving in travel time on average relative to a stop control when the vehicles obey the Nash equilibrium of the game. Advisors/Committee Members: Besieris, Ioannis M. (committeechair), Rakha, Hesham A. (committeechair), Abbott, Amos L. (committee member), Khedr, Mohamed Essam (committee member), Guo, Feng (committee member).

Subjects/Keywords: Machine learning; statistical learning; ITS; Bottlenecks Identification and Prediction; Dynamic Travel Time Prediction; Stop-run Driver Behavior Modeling; and uncontrolled intersection

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APA (6^th Edition):

Elhenawy, M. M. Z. (2015). Appling Machine and Statistical Learning Techniques to Intelligent Transport Systems: Bottleneck Identification and Prediction, Dynamic Travel Time Prediction, Driver Run-Stop Behavior Modeling, and Autonomous Vehicle Control at Intersections. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73790

Chicago Manual of Style (16^th Edition):

Elhenawy, Mohammed Mamdouh Zakaria. “Appling Machine and Statistical Learning Techniques to Intelligent Transport Systems: Bottleneck Identification and Prediction, Dynamic Travel Time Prediction, Driver Run-Stop Behavior Modeling, and Autonomous Vehicle Control at Intersections.” 2015. Doctoral Dissertation, Virginia Tech. Accessed March 07, 2021. http://hdl.handle.net/10919/73790.

MLA Handbook (7^th Edition):

Elhenawy, Mohammed Mamdouh Zakaria. “Appling Machine and Statistical Learning Techniques to Intelligent Transport Systems: Bottleneck Identification and Prediction, Dynamic Travel Time Prediction, Driver Run-Stop Behavior Modeling, and Autonomous Vehicle Control at Intersections.” 2015. Web. 07 Mar 2021.

Vancouver:

Elhenawy MMZ. Appling Machine and Statistical Learning Techniques to Intelligent Transport Systems: Bottleneck Identification and Prediction, Dynamic Travel Time Prediction, Driver Run-Stop Behavior Modeling, and Autonomous Vehicle Control at Intersections. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10919/73790.

Council of Science Editors:

Elhenawy MMZ. Appling Machine and Statistical Learning Techniques to Intelligent Transport Systems: Bottleneck Identification and Prediction, Dynamic Travel Time Prediction, Driver Run-Stop Behavior Modeling, and Autonomous Vehicle Control at Intersections. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/73790

University of Michigan

18. Xu, Zhengtian. On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures.

Degree: PhD, Civil Engineering, 2020, University of Michigan

URL: http://hdl.handle.net/2027.42/163209

► The proliferation of smartphones in recent years has catalyzed the rapid growth of ride-sourcing services such as Uber, Lyft, and Didi Chuxing. Such on-demand e-hailing… (more)

▼ The proliferation of smartphones in recent years has catalyzed the rapid growth of ride-sourcing services such as Uber, Lyft, and Didi Chuxing. Such on-demand e-hailing services significantly reduce the meeting frictions between drivers and riders and provide the platform with unprecedented flexibility and challenges in system management. A big issue that arises with service expansion is the empty miles produced by ride-sourcing vehicles. To overcome the physical and temporal frictions that separate drivers from customers and effectively reposition themselves towards desired destinations, ride-sourcing vehicles generate a significant number of vacant trips. These empty miles traveled result in inefficient use of the available fleet and increase traffic demand, posing substantial impacts on system operations. To tackle the issues, my dissertation is dedicated to deepening our understanding of the formation and the externalities of empty miles, and then proposing countermeasures to bolster system performance. There are two essential and interdependent contributors to empty miles generated by ride-sourcing vehicles: cruising in search of customers and deadheading to pick them up, which are markedly dictated by forces from riders, drivers, the platform, and policies imposed by regulators. In this dissertation, we structure our study of this complex process along three primary axes, respectively centered on the strategies of a platform, the behaviors of drivers, and the concerns of government agencies. In each axis, theoretical models are established to help understand the underlying physics and identify the trade-offs and potential issues that drive behind the empty miles. Massive data from Didi Chuxing, a dominant ride-sourcing company in China, are leveraged to evidence the presence of matters discussed in reality. Countermeasures are then investigated to strengthen management upon the empty miles, balance the interests of different stakeholders, and improve the system performance. Although this dissertation scopes out ride-sourcing services, the models, analyses, and solutions can be readily adapted to address related issues in other types of shared-use mobility services. Advisors/Committee Members: Yin, Yafeng (committee member), Saigal, Romesh (committee member), Chao, Xiuli (committee member), Masoud, Neda (committee member).

Subjects/Keywords: ride-sourcing systems and services; system modeling and analysis; driver behavior and management; market equilibrium and regulation; Transportation; Engineering

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APA (6^th Edition):

Xu, Z. (2020). On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/163209

Chicago Manual of Style (16^th Edition):

Xu, Zhengtian. “On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures.” 2020. Doctoral Dissertation, University of Michigan. Accessed March 07, 2021. http://hdl.handle.net/2027.42/163209.

MLA Handbook (7^th Edition):

Xu, Zhengtian. “On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures.” 2020. Web. 07 Mar 2021.

Vancouver:

Xu Z. On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2027.42/163209.

Council of Science Editors:

Xu Z. On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/163209

Georgia Tech

19. Yu, Huan. Behavioral modeling of drivers and oscillators using machine learning.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/64031

► The objective of this dissertation is to develop time-domain behavioral models for I/O drivers and oscillators for fast simulation and IP protection. For oscillators, augmented… (more)

Subjects/Keywords: Behavioral modeling; voltage-controlled oscillator (VCO); neural network; output buffer; pre-emphasis driver; input/output buffer modeling; signal and power integrity; control parameters; overclocking; Verilog-A

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APA (6^th Edition):

Yu, H. (2019). Behavioral modeling of drivers and oscillators using machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64031

Chicago Manual of Style (16^th Edition):

Yu, Huan. “Behavioral modeling of drivers and oscillators using machine learning.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 07, 2021. http://hdl.handle.net/1853/64031.

MLA Handbook (7^th Edition):

Yu, Huan. “Behavioral modeling of drivers and oscillators using machine learning.” 2019. Web. 07 Mar 2021.

Vancouver:

Yu H. Behavioral modeling of drivers and oscillators using machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1853/64031.

Council of Science Editors:

Yu H. Behavioral modeling of drivers and oscillators using machine learning. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/64031

Virginia Tech

20. Sangster, John David. Naturalistic Driving Data for the Analysis of Car-Following Models.

Degree: MS, Civil Engineering, 2011, Virginia Tech

URL: http://hdl.handle.net/10919/76925

► The driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating… (more)

Subjects/Keywords: traffic modeling; traffic flow theory; naturalistic driver behavior; car-following modeling

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APA (6^th Edition):

Sangster, J. D. (2011). Naturalistic Driving Data for the Analysis of Car-Following Models. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/76925

Chicago Manual of Style (16^th Edition):

Sangster, John David. “Naturalistic Driving Data for the Analysis of Car-Following Models.” 2011. Masters Thesis, Virginia Tech. Accessed March 07, 2021. http://hdl.handle.net/10919/76925.

MLA Handbook (7^th Edition):

Sangster, John David. “Naturalistic Driving Data for the Analysis of Car-Following Models.” 2011. Web. 07 Mar 2021.

Vancouver:

Sangster JD. Naturalistic Driving Data for the Analysis of Car-Following Models. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10919/76925.

Council of Science Editors:

Sangster JD. Naturalistic Driving Data for the Analysis of Car-Following Models. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/76925

21. Phan, Minh Tien. Estimation of driver awareness of pedestrian for an augmented reality advanced driving assistance system : Estimation de l’inattention du conducteur vis-à-vis d’un piéton pour un système d’aide à la conduite avancé utilisant la réalité augmentée.

Degree: Docteur es, Technologies de l'Information et des Systèmes : Unité de recherche Heudyasic [UMR-7253], 2016, Compiègne

URL: http://www.theses.fr/2016COMP2280

► La réalité augmentée (Augmented Reality ou AR) peut potentiellement changer significativement l’expérience utilisateur. Au contraire les applications sur Smartphone ou tablette, les technologies d’affichage tête… (more)

▼ La réalité augmentée (Augmented Reality ou AR) peut potentiellement changer significativement l’expérience utilisateur. Au contraire les applications sur Smartphone ou tablette, les technologies d’affichage tête haute (Head Up Display ouHUD) aujourd’hui sont capables de projeter localement sur une zone du pare-brise ou globalement sur tout le pare-brise. Le conducteur peut alors percevoir l’information directement dans son champ de vision. Ce ne sont pas que les informations basiques comme vitesse ou navigation, le système peut aussi afficher des aides, des indicateurs qui guident l’attention du conducteur vers les dangers possibles. Il existe alors un chalenge scientifique qui est de concevoir des visualisations d’interactions qui s’adaptent en fonction de l’observation de la scène mais aussi en fonction de l’observation du conducteur. Dans le contexte des systèmes d’alerte de collision avec les piétons (Pedestrian Collision Warning System ou PCWS), l’efficacité de la détection du piéton a atteint un niveau élevé grâce à la technologie de vision. Pourtant, les systèmes d’alerte ne s’adaptent pas au conducteur et à la situation, ils deviennent alors une source de distraction et sont souvent négligés par le conducteur. Pour ces raisons, ce travail de thèse consiste à proposer un nouveau concept de PCWS avec l’AR (nommé the AR-PCW system). Premièrement, nous nous concentrons sur l’étude de la conscience de la situation (Situation Awareness ou SA) du conducteur lorsqu’il y a un piéton présent devant le véhicule. Nous proposons une approche expérimentale pour collecter les données qui représentent l’attention du conducteur vis-à-vis du piéton (Driver Awareness of Pedestrian ou DAP) et l’inattention du conducteur vis-à-vis de celui-ci (Driver Unawareness of Pedestrian ou DUP). Ensuite, les algorithmes basées sur les charactéristiques, les modèles d’apprentissage basés sur les modèles discriminants (ex, Support Vector Machine ou SVM) ou génératifs (Hidden Markov Model ou HMM) sont proposés pour estimer le DUP et le DAP. La décision de notre AR-PCW system est effectivement basée sur ce modèle. Deuxièmement, nous proposons les aides ARs pour améliorer le DAP après une étude de l’état de l’art sur les ARs dans le contexte de la conduite automobile. La boite englobante autour du piéton et le panneau d’alerte de danger sont utilisés. Finalement, nous étudions expérimentalement notre système AR-PCW en analysant les effets des aides AR sur le conducteur. Un simulateur de conduite est utilisé et la simulation d’une zone HUD dans la scène virtuelle sont proposés. Vingt-cinq conducteurs de 2 ans de permis de conduite ont participé à l’expérimentation. Les situations ambigües sont créées dans le scénario de conduite afin d’analyser le DAP. Le conducteur doit suivre un véhicule et les piétons apparaissent à différents moments. L’effet des aides AR sur le conducteur est analysé à travers ses performances à réaliser la tâche de poursuite et ses réactions qui engendrent le DAP. Les résultats objectifs et subjectifs montrent que les aides… Advisors/Committee Members: Frémont, Vincent (thesis director), Thouvenin, Indira (thesis director).

Subjects/Keywords: Système d'alerte anticollision avec piétons; Conscience de la situation; Modélisation des comportements du conducteur; Situation awareness; Machine learning; Augmented reality; Driver behavior modeling; Pedestrian collision warning system; Driving simulator; Human machine interaction; Advanced driver assistance system

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Phan, M. T. (2016). Estimation of driver awareness of pedestrian for an augmented reality advanced driving assistance system : Estimation de l’inattention du conducteur vis-à-vis d’un piéton pour un système d’aide à la conduite avancé utilisant la réalité augmentée. (Doctoral Dissertation). Compiègne. Retrieved from http://www.theses.fr/2016COMP2280

Chicago Manual of Style (16^th Edition):

Phan, Minh Tien. “Estimation of driver awareness of pedestrian for an augmented reality advanced driving assistance system : Estimation de l’inattention du conducteur vis-à-vis d’un piéton pour un système d’aide à la conduite avancé utilisant la réalité augmentée.” 2016. Doctoral Dissertation, Compiègne. Accessed March 07, 2021. http://www.theses.fr/2016COMP2280.

MLA Handbook (7^th Edition):

Phan, Minh Tien. “Estimation of driver awareness of pedestrian for an augmented reality advanced driving assistance system : Estimation de l’inattention du conducteur vis-à-vis d’un piéton pour un système d’aide à la conduite avancé utilisant la réalité augmentée.” 2016. Web. 07 Mar 2021.

Vancouver:

Phan MT. Estimation of driver awareness of pedestrian for an augmented reality advanced driving assistance system : Estimation de l’inattention du conducteur vis-à-vis d’un piéton pour un système d’aide à la conduite avancé utilisant la réalité augmentée. [Internet] [Doctoral dissertation]. Compiègne; 2016. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2016COMP2280.

Council of Science Editors:

Phan MT. Estimation of driver awareness of pedestrian for an augmented reality advanced driving assistance system : Estimation de l’inattention du conducteur vis-à-vis d’un piéton pour un système d’aide à la conduite avancé utilisant la réalité augmentée. [Doctoral Dissertation]. Compiègne; 2016. Available from: http://www.theses.fr/2016COMP2280

22. Yang, Hsin-Hsiang. Driver Models to Emulate Human Anomalous Behaviors Leading to Vehicle Lateral and Longitudinal Accidents.

Degree: PhD, Mechanical Engineering, 2010, University of Michigan

URL: http://hdl.handle.net/2027.42/77710

► A new driver model is developed to emulate anomalous driving behaviors. This driver model is developed based on the concept that a driver model that… (more)

▼ A new driver model is developed to emulate anomalous driving behaviors. This driver model is developed based on the concept that a driver model that achieves driving tasks could be perturbed to emulate anomalous behaviors like human drivers by considering humans’ inherent limitations or by incorporating error mechanisms. If driver limitations or error mechanisms are properly designed, the driver model can generate accident or near-accident behaviors that are of interest to engineers developing active safety. Driver limitations can be physical and/or mental. Those limitations may cause driving accidents and need to be considered in the model. Another major contributor of driving accidents is driving error. Most existing models focus on describing driver behavior under certain tasks, and few of them include driving errors. The main contribution of this study is to fulfill the missing link between modeling normal driving tasks and modeling driving accidents. The development of an architecture and modeling process for driver models that emulates anomalous behaviors will be provided. Despite our best effort, no research on such driver models is found in literature. The model architecture and modeling process will be demonstrated by two examples. Lateral disturbance rejection for a lane-keeping task is used to illustrate driver behavior under lateral disturbance. Another example studies the effect of driving errors during longitudinal car-following. The goal of the lateral driving example is to analyze crosswind induced vehicle stability problems and the driving accident induced by human driver limitations. Both numerical simulations and driving simulator experiments are conducted to collect lateral driving behaviors. Lateral normal driving behaviors and accident inducing behaviors are studied. A lateral driver model with accidents was developed and used to evaluate vehicle crosswind stability and active safety system design. In the second example, an errable driver model is constructed and uses to capture human/control interaction and thus accelerate the cw/ca system development process. Driver errors can be viewed as a recurring event. If proper human cognition/error mechanisms are included and proper probability distribution functions are used to introduce human errors, it is possible to reproduce accident/incident behavior that is statistically similar to field testing results. Advisors/Committee Members: Peng, Huei (committee member), Eustice, Ryan M. (committee member), Gordon, Timothy J. (committee member), Ulsoy, A. Galip (committee member).

Subjects/Keywords: Driver Modeling; Active Safety; Driving Behavior; Errable Driver Model; Mechanical Engineering; Engineering

…needed for the driving task. Modeling driving error is different from modeling driver… …modeling process for driver models that emulate anomalous behaviors will be provided. Despite our… …an optimization scheme. Finally, a more complicated concept for driver modeling was shown… …traffic flow. Other than measuring dynamic variables and modeling driver decisions as a… …Fig. 3.15 Linearized driver/vehicle system diagram…

10 more images…

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APA (6^th Edition):

Yang, H. (2010). Driver Models to Emulate Human Anomalous Behaviors Leading to Vehicle Lateral and Longitudinal Accidents. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77710

Chicago Manual of Style (16^th Edition):

Yang, Hsin-Hsiang. “Driver Models to Emulate Human Anomalous Behaviors Leading to Vehicle Lateral and Longitudinal Accidents.” 2010. Doctoral Dissertation, University of Michigan. Accessed March 07, 2021. http://hdl.handle.net/2027.42/77710.

MLA Handbook (7^th Edition):

Yang, Hsin-Hsiang. “Driver Models to Emulate Human Anomalous Behaviors Leading to Vehicle Lateral and Longitudinal Accidents.” 2010. Web. 07 Mar 2021.

Vancouver:

Yang H. Driver Models to Emulate Human Anomalous Behaviors Leading to Vehicle Lateral and Longitudinal Accidents. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2027.42/77710.

Council of Science Editors:

Yang H. Driver Models to Emulate Human Anomalous Behaviors Leading to Vehicle Lateral and Longitudinal Accidents. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77710

Penn State University

23. Thiruvengada Ramanujam, Hari H S. DEVELOPING A FORMALISM FOR GIBSON’S AFFORDANCES USING COLORED PETRI NETS .

Degree: 2008, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/7901

► Gibson (1979/1986) proposed affordance theory to represent and model what the environment offers an animal for good or ill. Since its inception by Gibson, affordance… (more)

▼ Gibson (1979/1986) proposed affordance theory to represent and model what the environment offers an animal for good or ill. Since its inception by Gibson, affordance theory has undergone several refinements. A few affordance theory-based formalisms are reviewed in this proposal to demonstrate their potential advantages and disadvantages and to motivate an overarching formalism to model problems within dynamic environments. The purpose of this research is to provide a computational formalism for Gibson’s affordance theory based on characteristics of dynamic environments to include concurrency, stochasticity and spatio-temporality. A Colored Petri Net (CPN)-based model is proposed as a suitable instrument for developing this formalism. A mathematical model, graphical representation and computational model for this CPN model is developed within the context of a driving problem. The affordances offered by this driving environment are analogous to those offered by a set of highway lanes. A formative analysis technique is also introduced along with an overall data analysis procedure to analyze the precision of the actualized actions and the niche of lane affordances that become available to the driver within the highway lane-driver system. An empirical study was conducted using a team of two expert drivers to elicit various behaviors that would help resolve the precision of the CPN model. Four output metrics were defined that represent the deviation between the empirical human performance and model predicted data: lane position, turn direction of the subject driver’s vehicle, time taken by the subject driver to move from the starting lane to the exit lane and the total utilization of the exit lane by the subject driver. The significance of the results is then explained with reference to research implications and future work. Advisors/Committee Members: Ling Rothrock, Committee Chair/Co-Chair, Gwendolyn E Campbell, Committee Member, Richard Allen Wysk, Committee Member, M Jeya Chandra, Committee Member, Sean N Brennan, Committee Member.

Subjects/Keywords: Human Behavior Modeling; Performance Evaluation; Driver Behavior; CPN; Colored Petri Net; Affordance; Simulation

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APA (6^th Edition):

Thiruvengada Ramanujam, H. H. S. (2008). DEVELOPING A FORMALISM FOR GIBSON’S AFFORDANCES USING COLORED PETRI NETS . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/7901

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Thiruvengada Ramanujam, Hari H S. “DEVELOPING A FORMALISM FOR GIBSON’S AFFORDANCES USING COLORED PETRI NETS .” 2008. Thesis, Penn State University. Accessed March 07, 2021. https://submit-etda.libraries.psu.edu/catalog/7901.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Thiruvengada Ramanujam, Hari H S. “DEVELOPING A FORMALISM FOR GIBSON’S AFFORDANCES USING COLORED PETRI NETS .” 2008. Web. 07 Mar 2021.

Vancouver:

Thiruvengada Ramanujam HHS. DEVELOPING A FORMALISM FOR GIBSON’S AFFORDANCES USING COLORED PETRI NETS . [Internet] [Thesis]. Penn State University; 2008. [cited 2021 Mar 07]. Available from: https://submit-etda.libraries.psu.edu/catalog/7901.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Thiruvengada Ramanujam HHS. DEVELOPING A FORMALISM FOR GIBSON’S AFFORDANCES USING COLORED PETRI NETS . [Thesis]. Penn State University; 2008. Available from: https://submit-etda.libraries.psu.edu/catalog/7901

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Vyas, Gaurav. A joint vehicle holdings (type and vintage) and primary driver assignment model with an application for California.

Degree: MSin Engineering, Civil Engineering, 2011, University of Texas – Austin

URL: http://hdl.handle.net/2152/15711

► Transportation sector has been a major contributing factor to the overall emissions of most pollutants and thus their impacts on the environment. Among all transportation… (more)

▼ Transportation sector has been a major contributing factor to the overall emissions of most pollutants and thus their impacts on the environment. Among all transportation activities, on-road travel accounts for most part of the Greenhouse gas (GHG) emissions and fuel use. It also has a very un-desirable impact on the transportation network conditions increasing the traffic congestion levels. The main aim of transportation planning agencies is to implement the policy changes that will reduce automobile dependency and increase transit and non-motorized modes usage. However, planning agencies can come up with proactive economic, land-use and transportation policies provided they have a model which is sensitive to all the above mentioned factors to predict the vehicle fleet composition and usage of households. Moreover, the type of vehicle that a household gets (vehicle type choice) and the annual mileage (usage) associated with that vehicle is very closely related to the person in the household who uses that vehicle the most (allocation to primary driver). So, it is no longer possible to view all these decisions separately. Instead, we need to model all these decisions- vehicle type choice, usage, and allocation to primary driver simultaneously at a household level. In this study, we estimate and apply a joint household-level model of the number of vehicles owned by the household, the vehicle type choice of each vehicle, the annual mileage on each vehicle, as well as the individual assigned as the primary driver for each vehicle. A version of the proposed model system currently serves as the engine for a household vehicle composition and evolution simulator, which itself has been embedded within the larger SimAGENT (for Simulator of Activities, Greenhouse emissions, Networks, and Travel) activity-based travel and emissions forecasting system for the Southern California Association of Governments (SCAG) planning region. Advisors/Committee Members: Bhat, Chandra R. (Chandrasekhar R.), 1964- (advisor).

Subjects/Keywords: Car ownership model; Primary driver allocation; MDCEV; Activity based modeling

…Estimation Results of MNL Component for Primary Driver Allocation… …35 ix Chapter 1: Introduction In regional travel modeling and simulation, the… …travel demand modeling and transportation policy analysis. To be sure, the importance of… …modeling household vehicle fleet choices has been recognized for several decades now, though the… …broad context of the methodological challenge of modeling all dimensions of all vehicles owned…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vyas, G. (2011). A joint vehicle holdings (type and vintage) and primary driver assignment model with an application for California. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/15711

Chicago Manual of Style (16^th Edition):

Vyas, Gaurav. “A joint vehicle holdings (type and vintage) and primary driver assignment model with an application for California.” 2011. Masters Thesis, University of Texas – Austin. Accessed March 07, 2021. http://hdl.handle.net/2152/15711.

MLA Handbook (7^th Edition):

Vyas, Gaurav. “A joint vehicle holdings (type and vintage) and primary driver assignment model with an application for California.” 2011. Web. 07 Mar 2021.

Vancouver:

Vyas G. A joint vehicle holdings (type and vintage) and primary driver assignment model with an application for California. [Internet] [Masters thesis]. University of Texas – Austin; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2152/15711.

Council of Science Editors:

Vyas G. A joint vehicle holdings (type and vintage) and primary driver assignment model with an application for California. [Masters Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/15711

25. Sierra Gonzalez, David. Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios : Vers une prédiction et une prise de décision inspirées de celles des humains pour la conduite automatisée de véhicules sur autoroute.

Degree: Docteur es, Informatique, 2019, Université Grenoble Alpes (ComUE)

URL: http://www.theses.fr/2019GREAM012

► Au cours des dernières décennies, les constructeurs automobiles ont constamment introduit des innovations technologiques visant à rendre les véhicules plus sûrs. Le niveau de sophistication… (more)

▼ Au cours des dernières décennies, les constructeurs automobiles ont constamment introduit des innovations technologiques visant à rendre les véhicules plus sûrs. Le niveau de sophistication de ces systèmes avancés d’aide à la conduite s’est accru parallèlement aux progrès de la technologie des capteurs et de la puissance informatique intégrée. Plus récemment, une grande partie de la recherche effectuée par l'industrie et les institutions s'est concentrée sur l'obtention d'une conduite entièrement automatisée. Les avantages sociétaux potentiels de cette technologie sont nombreux, notamment des routes plus sûres, des flux de trafic améliorés et une mobilité accrue pour les personnes âgées et les handicapés. Toutefois, avant que les véhicules autonomes puissent être commercialisés, ils doivent pouvoir partager la route en toute sécurité avec d’autres véhicules conduits par des conducteurs humains. En d'autres termes, ils doivent pouvoir déduire l'état et les intentions du trafic environnant à partir des données brutes fournies par divers capteurs embarqués, et les utiliser afin de pouvoir prendre les bonnes décisions de conduite sécurisée. Malgré la complexité apparente de cette tâche, les conducteurs humains ont la capacité de prédire correctement l’évolution du trafic environnant dans la plupart des situations. Cette capacité de prédiction est rendu plus simple grâce aux règles imposées par le code de la route qui limitent le nombre d’hypothèses; elle repose aussi sur l’expérience du conducteur en matière d’évaluation et de réduction du risque. L'absence de cette capacité à comprendre naturellement une scène de trafic constitue peut-être, le principal défi qui freine le déploiement à grande échelle de véhicules véritablement autonomes sur les routes.Dans cette thèse, nous abordons les problèmes de modélisation du comportement du conducteur, d'inférence sur le comportement des autres véhicules, et de la prise de décision pour la navigation sûre. En premier lieu, nous modélisons automatiquement le comportement d'un conducteur générique à partir de données de conduite démontrées, évitant ainsi le réglage manuel traditionnel des paramètres du modèle. Ce modèle codant les préférences d’un conducteur par rapport au réseau routier (par exemple, voie ou vitesse préférées) et aux autres usagers de la route (par exemple, distance préférée au véhicule de devant). Deuxièmement, nous décrivons une méthode qui utilise le modèle appris pour prédire la séquence des actions à long terme de tout conducteur dans une scène de trafic. Cette méthode de prédiction suppose que tous les acteurs du trafic se comportent de manière aversive au risque, et donc ne peut pas prévoir les manœuvres dangereux ou les accidents. Pour pouvoir traiter de tels cas, nous proposons un modèle probabiliste plus sophistiqué, qui estime l'état et les intentions du trafic environnant en combinant la prédiction basée sur le modèle avec les preuves dynamiques fournies par les capteurs. Le modèle proposé imite ainsi en quelque sorte le processus de raisonnement des… Advisors/Committee Members: Mazer, Emmanuel (thesis director), Laugier, Christian (thesis director).

Subjects/Keywords: Navigation en milieu humain; Prise de décision dans l’incertain; Prédiction de mouvement; Modélisation et apprentissage de comportements; Motion prediction; Driver behavior modeling; Decision-Making under uncertainty; 004.019

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sierra Gonzalez, D. (2019). Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios : Vers une prédiction et une prise de décision inspirées de celles des humains pour la conduite automatisée de véhicules sur autoroute. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2019GREAM012

Chicago Manual of Style (16^th Edition):

Sierra Gonzalez, David. “Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios : Vers une prédiction et une prise de décision inspirées de celles des humains pour la conduite automatisée de véhicules sur autoroute.” 2019. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed March 07, 2021. http://www.theses.fr/2019GREAM012.

MLA Handbook (7^th Edition):

Sierra Gonzalez, David. “Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios : Vers une prédiction et une prise de décision inspirées de celles des humains pour la conduite automatisée de véhicules sur autoroute.” 2019. Web. 07 Mar 2021.

Vancouver:

Sierra Gonzalez D. Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios : Vers une prédiction et une prise de décision inspirées de celles des humains pour la conduite automatisée de véhicules sur autoroute. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2019. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2019GREAM012.

Council of Science Editors:

Sierra Gonzalez D. Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios : Vers une prédiction et une prise de décision inspirées de celles des humains pour la conduite automatisée de véhicules sur autoroute. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2019. Available from: http://www.theses.fr/2019GREAM012

26. Fuller, Helen J. A. The Virtual Driver: Integrating Physical and Cognitive Human Models to Simulate Driving with a Secondary In-Vehicle Task.

Degree: PhD, Biomedical Engineering, 2010, University of Michigan

URL: http://hdl.handle.net/2027.42/75847

► Models of human behavior provide insight into people’s choices and actions and form the basis of engineering tools for predicting performance and improving interface design.… (more)

▼ Models of human behavior provide insight into people’s choices and actions and form the basis of engineering tools for predicting performance and improving interface design. Most human models are either cognitive, focusing on the information processing underlying the decisions made when performing a task, or physical, representing postures and motions used to perform the task. In general, cognitive models contain a highly simplified representation of the physical aspects of a task and are best suited for analysis of tasks with only minor motor components. Physical models require a person experienced with the task and the software to enter detailed information about how and when movements should be made, a process that can be costly, time consuming, and inaccurate. Many tasks have both cognitive and physical components, which may interact in ways that could not be predicted using a cognitive or physical model alone. This research proposes a solution by combining a cognitive model, the Queuing Network – Model Human Processor, and a physical model, the Human Motion Simulation (HUMOSIM) Framework, to produce an integrated cognitive-physical human model that makes it possible to study complex human-machine interactions. The physical task environment is defined using the HUMOSIM Framework, which communicates relevant information such as movement times and difficulty to the QN-MHP. Action choice and movement sequencing are performed in the QN-MHP. The integrated model’s more natural movements, generated by motor commands from the QN-MHP, and more realistic cognitive decisions, made using physical information from the Framework, make it useful for evaluating different designs for tasks, spaces, systems, and jobs. The Virtual Driver is the application of the integrated model to driving with an in-vehicle task. A driving simulator experiment was used to tune and evaluate the integrated model. Increasing the visual and physical difficulty of the in-vehicle task affected the resource-sharing strategies drivers used and resulted in deterioration in driving and in-vehicle task performance, especially for shorter drivers. The Virtual Driver replicates basic driving, in-vehicle task, and resource-sharing behaviors and provides a new way to study driver distraction. The model has applicability to interface design and predictions about staffing requirements and performance. Advisors/Committee Members: Liu, Yili (committee member), Reed, Matthew Paul (committee member), Chaffin, Don B. (committee member), Martin, Bernard J. (committee member), Sienko, Kathleen Helen (committee member).

Subjects/Keywords: Human Modeling; Cognitive Modeling; Driver Distraction; Integrated Human Model; QN-MHP; HUMOSIM Framework; Biomedical Engineering; Engineering

…95 Chapter 5 Modeling the Convoy Experiment with the Virtual Driver Model… …59 Chapter 4 Virtual Driver Model… …80 vii Physical Capabilities of Driver… …81 The Virtual Driver: Integration of Cognitive Model and Physical Model… …82 Modeling Driving with an In-Vehicle Task…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Fuller, H. J. A. (2010). The Virtual Driver: Integrating Physical and Cognitive Human Models to Simulate Driving with a Secondary In-Vehicle Task. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/75847

Chicago Manual of Style (16^th Edition):

Fuller, Helen J A. “The Virtual Driver: Integrating Physical and Cognitive Human Models to Simulate Driving with a Secondary In-Vehicle Task.” 2010. Doctoral Dissertation, University of Michigan. Accessed March 07, 2021. http://hdl.handle.net/2027.42/75847.

MLA Handbook (7^th Edition):

Fuller, Helen J A. “The Virtual Driver: Integrating Physical and Cognitive Human Models to Simulate Driving with a Secondary In-Vehicle Task.” 2010. Web. 07 Mar 2021.

Vancouver:

Fuller HJA. The Virtual Driver: Integrating Physical and Cognitive Human Models to Simulate Driving with a Secondary In-Vehicle Task. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2027.42/75847.

Council of Science Editors:

Fuller HJA. The Virtual Driver: Integrating Physical and Cognitive Human Models to Simulate Driving with a Secondary In-Vehicle Task. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/75847

27. Tielman, W.L. An Agent-based Approach to Simulating Train Driver Behaviour.

Degree: 2015, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/307355

► Railway simulations have been a useful tool within the railway industry. One of the simulators that ProRail, the Dutch infrastructure manager for the railways, uses… (more)

Subjects/Keywords: agents; simulation; railway; modeling human operator; artificial Intelligence; train driver behaviour; machine learning

…of train driver behaviour. Agent based simulation has been noted as a powerful simulation… …driver implementation in order to do future research into the effects of adaptations to… …and implementing this train driver agent will be lined out, from modelling to… …experimentation. 1.2 Problem In this project a train driver agent was to be developed in order to find… …driver behaviour to a micro-level simulator (FRISO), using an Agent based approach…

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APA (6^th Edition):

Tielman, W. L. (2015). An Agent-based Approach to Simulating Train Driver Behaviour. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/307355

Chicago Manual of Style (16^th Edition):

Tielman, W L. “An Agent-based Approach to Simulating Train Driver Behaviour.” 2015. Masters Thesis, Universiteit Utrecht. Accessed March 07, 2021. http://dspace.library.uu.nl:8080/handle/1874/307355.

MLA Handbook (7^th Edition):

Tielman, W L. “An Agent-based Approach to Simulating Train Driver Behaviour.” 2015. Web. 07 Mar 2021.

Vancouver:

Tielman WL. An Agent-based Approach to Simulating Train Driver Behaviour. [Internet] [Masters thesis]. Universiteit Utrecht; 2015. [cited 2021 Mar 07]. Available from: http://dspace.library.uu.nl:8080/handle/1874/307355.

Council of Science Editors:

Tielman WL. An Agent-based Approach to Simulating Train Driver Behaviour. [Masters Thesis]. Universiteit Utrecht; 2015. Available from: http://dspace.library.uu.nl:8080/handle/1874/307355

28. Mao, Huiying. Optimal Driver Risk Modeling.

Degree: PhD, Statistics, 2019, Virginia Tech

URL: http://hdl.handle.net/10919/93211

► When riding in a vehicle, it is common to have personal judgement about whether the driver is safe or risky. The drivers’ behavior may affect… (more)

Subjects/Keywords: Decision-adjusted Modeling; Driver Risk; Kinematic; Naturalistic Driving Study; Rare Events; Traffic Safety

…precise and optimal driver risk modeling procedure using the multi-dimensional and large-scale… …modeling Statistical modeling and analysis of driver-related data plays an important role for… …27 2.5 e − β, b for SHRP 2 driver data 31 Correction magnitude of regression coefficients… …SHRP 2 driver data… …31 2.7 g − RR, d for SHRP 2 driver data . . . Correction magnitude of rate ratios, RR 32…

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APA (6^th Edition):

Mao, H. (2019). Optimal Driver Risk Modeling. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93211

Chicago Manual of Style (16^th Edition):

Mao, Huiying. “Optimal Driver Risk Modeling.” 2019. Doctoral Dissertation, Virginia Tech. Accessed March 07, 2021. http://hdl.handle.net/10919/93211.

MLA Handbook (7^th Edition):

Mao, Huiying. “Optimal Driver Risk Modeling.” 2019. Web. 07 Mar 2021.

Vancouver:

Mao H. Optimal Driver Risk Modeling. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10919/93211.

Council of Science Editors:

Mao H. Optimal Driver Risk Modeling. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93211

29. Borra, Venkata Shesha Vamsi. Design and Modeling of High Performance LED Dimming Driver with Reduced CurrentSpikes using Turn-On Snubber across Power MOSFET.

Degree: MSin Engineering, Department of Electrical and Computer Engineering, 2014, Youngstown State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1402958388

► The LEVEL-3 power MOSFET SPICE model (IRF034) is used to design and model the LED driver. Three LED drivers are designed and their efficiency values… (more)

Subjects/Keywords: Electrical Engineering; Dimming; Pulse Width Modulation; LED Driver; Modeling; PSpice; MOSFET; Snubber circuit; efficiency

…58 Figure 42: Driver Circuit 2 modeling OSLON LA H9GP… …PWM). This thesis inclines towards designing and modeling an LED dimming driver using… …efficiency of the driver circuit is discussed in terms of modeling the LEDs SPICE parameters to… …for designing the LED driver and modeling the diode SPICE models. 12 2.2.1 Diode SPICE… …semiconductors is included in the LED modeling to estimate the real efficiency of the designed driver…

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APA (6^th Edition):

Borra, V. S. V. (2014). Design and Modeling of High Performance LED Dimming Driver with Reduced CurrentSpikes using Turn-On Snubber across Power MOSFET. (Masters Thesis). Youngstown State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ysu1402958388

Chicago Manual of Style (16^th Edition):

Borra, Venkata Shesha Vamsi. “Design and Modeling of High Performance LED Dimming Driver with Reduced CurrentSpikes using Turn-On Snubber across Power MOSFET.” 2014. Masters Thesis, Youngstown State University. Accessed March 07, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1402958388.

MLA Handbook (7^th Edition):

Borra, Venkata Shesha Vamsi. “Design and Modeling of High Performance LED Dimming Driver with Reduced CurrentSpikes using Turn-On Snubber across Power MOSFET.” 2014. Web. 07 Mar 2021.

Vancouver:

Borra VSV. Design and Modeling of High Performance LED Dimming Driver with Reduced CurrentSpikes using Turn-On Snubber across Power MOSFET. [Internet] [Masters thesis]. Youngstown State University; 2014. [cited 2021 Mar 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1402958388.

Council of Science Editors:

Borra VSV. Design and Modeling of High Performance LED Dimming Driver with Reduced CurrentSpikes using Turn-On Snubber across Power MOSFET. [Masters Thesis]. Youngstown State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1402958388

Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ)

30. Πάνου, Μαρία. Προηγμένο σύστημα εξατομικευμένης πληροφόρησης και προειδοποίησης των μετακινουμένων.

Degree: 2007, Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ)

URL: http://hdl.handle.net/10442/hedi/20593

► Several studies on the use of web services and specifically those related to infomobility services, prove their fast penetration to the market and foresee their… (more)

▼ Several studies on the use of web services and specifically those related to infomobility services, prove their fast penetration to the market and foresee their high increase over the next years. Such services are offered through the mobile phone or PDA, where the travelers receive location-based information. On the other hand, that gradual difusion of Advanced Driver Information Systems (ADAS) and their integration by the vehicle manufacturers aims at the enhancement of traffic safety. However, the benefits of such systems to the traffic safety may be significantly reduced by the inappropriate behaviour of the drivers towards the relevant technologies, e.g. overconfidence to the system that leads to the reduction of attention on the road or behavioural adaptation that compensates the additional safety aspects. Systems with automatic adaptation capabilities, according to the ‘needs and wants’ of each driver, constitute an optimal solution, reducing the driver workload by using ADAS and increasing his/her comfort while driving. The aim of the current PhD dissertation is the optimization of infomobility services for travelers, as welll as drivers warning systems regarding danger stemming both from the longitudinal and lateral road axes, through their personalization. Thus, particular algorithms are proposed, based upon a number of relevant parameters (static, semidynamic and dynamic), leading to a holistic personalization. The drivers’ warning algorithms are based on dynamic parameters that determine their personal driving style, being the reaction time, the distance to line crossing (or alternatively the time to line crossing) and the time to collision or time headway. The infomobility personalisation algorithms for travelers are based on the user’s previous selections and are distinguished as follows: route selection information, points-of-interest (POIs) selection information and pushed information (by the system itself, without prior request by the user). The parameters that constitute the route selection algorithm are the maximum accepted walking distance per transportation means connection, the accepted travel means and the maximum accepted number of transportation mean changes during a multimodal trip. Secondary parameters are also taken into account, which are the shortest or the cheapest route. The parameters used for the POI selection personalization are the POI type (hotel, restaurant, etc.), its sub-type (e.g. restaurant type: Italian, traditional, Chinese, etc.), the day of the week (either weekday or weekend) and of course the user’s past selections. The last parameter is also used for the personalization of the “pushed” service (service proposed to the user without explicitly asking for them). Following, a short description of the dissertation contents is given. Chapter 1 presents the market situation and trends in relation to the location-based infomobility services through the mobile devices, as well as the increasing use of ADAS and the need for their adaptation and personalisaiton for the enhancement…

Subjects/Keywords: Μοντελοποίηση συμπεριφοράς οδηγού; Προσωποποιημένοι αλγόριθμοι ΣΣΥΟ προειδοποίησης οδηγών; Προσωποποιημένοι αλγόριθμοι πληροφόρησης μετακινουμένων; Εξατομικευμένο σύστημα προειδοποίησης οδηγών; Εξατομικευμένο σύστημα πληροφόρησης μετακινουμένων; Driver behaviour modeling; Personalised drivers warning algorithms; Personalised travellers information algorithms; Personalised drivers warning system; Personalised travellers information system

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Πάνου, . �. (2007). Προηγμένο σύστημα εξατομικευμένης πληροφόρησης και προειδοποίησης των μετακινουμένων. (Thesis). Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ). Retrieved from http://hdl.handle.net/10442/hedi/20593

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Πάνου, Μαρία. “Προηγμένο σύστημα εξατομικευμένης πληροφόρησης και προειδοποίησης των μετακινουμένων.” 2007. Thesis, Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ). Accessed March 07, 2021. http://hdl.handle.net/10442/hedi/20593.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Πάνου, Μαρία. “Προηγμένο σύστημα εξατομικευμένης πληροφόρησης και προειδοποίησης των μετακινουμένων.” 2007. Web. 07 Mar 2021.

Vancouver:

Πάνου �. Προηγμένο σύστημα εξατομικευμένης πληροφόρησης και προειδοποίησης των μετακινουμένων. [Internet] [Thesis]. Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ); 2007. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10442/hedi/20593.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Πάνου �. Προηγμένο σύστημα εξατομικευμένης πληροφόρησης και προειδοποίησης των μετακινουμένων. [Thesis]. Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ); 2007. Available from: http://hdl.handle.net/10442/hedi/20593

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations