subject:(driver behaviour)

University of South Africa

1. Solomon, Cleshain Theodore. Driver attention and behaviour monitoring with the Microsoft Kinect sensor .

Degree: 2015, University of South Africa

► Modern vehicles are designed to protect occupants in the event of a crash with some vehicles better at this than others. However, passenger protection during… (more)

Subjects/Keywords: Kinect; Driver fatigue; Driver behaviour; Driver distraction; Behaviour detection; Driver attention; Behaviour monitoring; Feature extraction; Face detection

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APA (6^th Edition):

Solomon, C. T. (2015). Driver attention and behaviour monitoring with the Microsoft Kinect sensor . (Masters Thesis). University of South Africa. Retrieved from http://hdl.handle.net/10500/21798

Chicago Manual of Style (16^th Edition):

Solomon, Cleshain Theodore. “Driver attention and behaviour monitoring with the Microsoft Kinect sensor .” 2015. Masters Thesis, University of South Africa. Accessed March 07, 2021. http://hdl.handle.net/10500/21798.

MLA Handbook (7^th Edition):

Solomon, Cleshain Theodore. “Driver attention and behaviour monitoring with the Microsoft Kinect sensor .” 2015. Web. 07 Mar 2021.

Vancouver:

Solomon CT. Driver attention and behaviour monitoring with the Microsoft Kinect sensor . [Internet] [Masters thesis]. University of South Africa; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10500/21798.

Council of Science Editors:

Solomon CT. Driver attention and behaviour monitoring with the Microsoft Kinect sensor . [Masters Thesis]. University of South Africa; 2015. Available from: http://hdl.handle.net/10500/21798

University of Waterloo

2. Ou, Chaojie. Deep Learning-based Driver Behavior Modeling and Analysis.

Degree: 2019, University of Waterloo

URL: http://hdl.handle.net/10012/15334

► Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have… (more)

▼ Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have increased drastically over the years. In the past decades, there has been a remarkable push towards designing and developing new driver assistance systems with much better recognition and prediction capabilities. Equipped with various sensory systems, these Advanced Driver Assistance Systems (ADAS) are able to accurately perceive information on road conditions, predict traffic situations, estimate driving risks, and provide drivers with imminent warnings and visual assistance. In this thesis, we focus on two main aspects of driver behavior modeling in the design of new generation of ADAS. We first aim at improving the generalization ability of driver distraction recognition systems to diverse driving scenarios using the latest tools of machine learning and connectionist modeling, namely deep learning. To this end, we collect a large dataset of images on various driving situations of drivers from the Internet. Then we introduce Generative Adversarial Networks (GANs) as a data augmentation tool to enhance detection accuracy. A novel driver monitoring system is also introduced. This monitoring system combines multi-information resources, including a driver distraction recognition system, to assess the danger levels of driving situations. Moreover, this thesis proposes a multi-modal system for distraction recognition under various lighting conditions and presents a new Convolutional Neural Network (CNN) architecture, which can operate real-time on a resources-limited computational platform. The new CNN is built upon a novel network bottleneck of Depthwise Separable Convolution layers. The second part of this thesis focuses on driver maneuver prediction, which infers the direction a driver will turn to before a green traffic light is on and predicts accurately whether or not he/she will change the current driving lane. Here, a new method to label driving maneuver records is proposed, by which driving feature sequences for the training of prediction systems are more closely related to their labels. To this end, a new prediction system, which is based on Quasi-Recurrent Neural Networks, is introduced. In addition, and as an application of maneuver prediction, a novel driving proficiency assessment method is proposed. This method exploits the generalization abilities of different maneuver prediction systems to estimate drivers' driving abilities, and it demonstrates several advantages against existing assessment methods. In conjunction with the theoretical contribution, a series of comprehensive experiments are conducted, and the proposed methods are assessed against state-of-the-art works. The analysis of experimental results shows the improvement of results as compared with existing techniques.

Subjects/Keywords: driver behavior; deep learning

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

APA (6^th Edition):

Ou, C. (2019). Deep Learning-based Driver Behavior Modeling and Analysis. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15334

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):

Ou, Chaojie. “Deep Learning-based Driver Behavior Modeling and Analysis.” 2019. Thesis, University of Waterloo. Accessed March 07, 2021. http://hdl.handle.net/10012/15334.

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

MLA Handbook (7^th Edition):

Ou, Chaojie. “Deep Learning-based Driver Behavior Modeling and Analysis.” 2019. Web. 07 Mar 2021.

Vancouver:

Ou C. Deep Learning-based Driver Behavior Modeling and Analysis. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10012/15334.

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

Council of Science Editors:

Ou C. Deep Learning-based Driver Behavior Modeling and Analysis. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/15334

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

McMaster University

3. Dou, Yangliu. DRIVER BEHAVIOUR PREDICTION MODELS USING ARTIFICIAL INTELLIGENCE ALGORITHMS AND STATISTICAL MODELING.

Degree: PhD, 2019, McMaster University

URL: http://hdl.handle.net/11375/24195

►

To improve the safety and comfort of intelligent vehicles, advanced driver models offer promising solutions. However, several shortcomings of these models prevent them from being… (more)

▼

To improve the safety and comfort of intelligent vehicles, advanced driver models offer promising solutions. However, several shortcomings of these models prevent them from being widely applied in reality. To address these shortcomings, advanced artificial intelligence algorithms in conjunction with the sufficient driving environmental factors are proposed based on real-life driving data. More specifically, three typical problems will be addressed in this thesis: Mandatory Lane Changing (MLC) suggestion at the highway entrance; Discretionary Lane Changing (DLC) intention prediction; Car-Following gap model considering the effect of cuts-in from the adjacent lanes. For the MLC suggestion system, in which the main challenges are efficient decision making and high prediction accuracy of both non-merge and merge events, an additional gated branch neural network (GBNN) is proposed. The proposed GBNN algorithm not only achieves the highest accuracy among conventional binary classifiers in terms of great performance on the non-merge accuracy, the merge accuracy, and receiver operating characteristic score but also takes less time. For the DLC, we propose a recurrent neural network (RNN)-based time series classifier with a gated recurrent units (GRU) architecture to predict the surrounding vehicles’ intention. It can predict the surrounding vehicles’ lane changing maneuver 0.8 s in advance at a recall and precision of 99.5% and 98.7%, respectively, which outperforms conventional algorithms such as the Hidden Markov Model (HMM). Finally, drivers are typically faced with two competing challenges when following a preceding vehicle. A method is proposed to address the problem through an overall objective function of car-following gap and velocity. Based on this, seeking the strategic car-following gap translates to finding the optimal solution that minimizes the overall objective function. With the support of field data, the method along with concrete models are instantiated and the application of the method is elaborated.

Thesis

Doctor of Philosophy (PhD)

Lane changing and car following are the two most frequently encountered driving behaviours for intelligent vehicles. Substantial research has been carried out and several prototypes have been developed by universities as well as companies. However, the low accuracy and high computational cost prevent the existing lane changing models from providing safer and more reliable decisions for intelligent vehicles. In the existing car-following models, there are also few models that consider the effects of cut-ins from adjacent lanes which may result in their poor accuracy and efficiency. To address these obstacles, advanced artificial intelligence algorithms combined with sufficient driving environmental factors are proposed due to their promise of providing accurate, efficient, and robust lane changing and car-following models. The main part of this thesis is composed of three journal papers. Paper 1 proposed a gated branch neural network for a mandatory lane changing…

Advisors/Committee Members: Yan, Fengjun, Mechanical Engineering.

Subjects/Keywords: DRIVER BEHAVIOUR PREDICTION MODELS; ARTIFICIAL INTELLIGENCE ALGORITHMS

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APA (6^th Edition):

Dou, Y. (2019). DRIVER BEHAVIOUR PREDICTION MODELS USING ARTIFICIAL INTELLIGENCE ALGORITHMS AND STATISTICAL MODELING. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/24195

Chicago Manual of Style (16^th Edition):

Dou, Yangliu. “DRIVER BEHAVIOUR PREDICTION MODELS USING ARTIFICIAL INTELLIGENCE ALGORITHMS AND STATISTICAL MODELING.” 2019. Doctoral Dissertation, McMaster University. Accessed March 07, 2021. http://hdl.handle.net/11375/24195.

MLA Handbook (7^th Edition):

Dou, Yangliu. “DRIVER BEHAVIOUR PREDICTION MODELS USING ARTIFICIAL INTELLIGENCE ALGORITHMS AND STATISTICAL MODELING.” 2019. Web. 07 Mar 2021.

Vancouver:

Dou Y. DRIVER BEHAVIOUR PREDICTION MODELS USING ARTIFICIAL INTELLIGENCE ALGORITHMS AND STATISTICAL MODELING. [Internet] [Doctoral dissertation]. McMaster University; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11375/24195.

Council of Science Editors:

Dou Y. DRIVER BEHAVIOUR PREDICTION MODELS USING ARTIFICIAL INTELLIGENCE ALGORITHMS AND STATISTICAL MODELING. [Doctoral Dissertation]. McMaster University; 2019. Available from: http://hdl.handle.net/11375/24195

Cranfield University

4. Anuar, Nur Khairiel. The impact of airport road wayfinding design on senior driver behaviour.

Degree: PhD, 2016, Cranfield University

URL: http://dspace.lib.cranfield.ac.uk/handle/1826/11806

► Airport road access wayfinding refers to a process in which a driver makes a decision to navigate using information support systems in order to arrive… (more)

Subjects/Keywords: Wayfinding; Airport; Senior driver; Driving behaviour; Simulation

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APA (6^th Edition):

Anuar, N. K. (2016). The impact of airport road wayfinding design on senior driver behaviour. (Doctoral Dissertation). Cranfield University. Retrieved from http://dspace.lib.cranfield.ac.uk/handle/1826/11806

Chicago Manual of Style (16^th Edition):

Anuar, Nur Khairiel. “The impact of airport road wayfinding design on senior driver behaviour.” 2016. Doctoral Dissertation, Cranfield University. Accessed March 07, 2021. http://dspace.lib.cranfield.ac.uk/handle/1826/11806.

MLA Handbook (7^th Edition):

Anuar, Nur Khairiel. “The impact of airport road wayfinding design on senior driver behaviour.” 2016. Web. 07 Mar 2021.

Vancouver:

Anuar NK. The impact of airport road wayfinding design on senior driver behaviour. [Internet] [Doctoral dissertation]. Cranfield University; 2016. [cited 2021 Mar 07]. Available from: http://dspace.lib.cranfield.ac.uk/handle/1826/11806.

Council of Science Editors:

Anuar NK. The impact of airport road wayfinding design on senior driver behaviour. [Doctoral Dissertation]. Cranfield University; 2016. Available from: http://dspace.lib.cranfield.ac.uk/handle/1826/11806

University of Lund

5. Larsson, Annika. Automation and the nature of driving - The effect of adaptive cruise control on drivers' tactical driving decisions.

Degree: 2013, University of Lund

URL: https://lup.lub.lu.se/record/4025423 ; https://portal.research.lu.se/ws/files/5375171/4025428.pdf

► Advanced driving assistance systems that offer support by operating for example longitudinal control will have an effect on the transport system. Previous studies have also… (more)

▼ Advanced driving assistance systems that offer support by operating for example longitudinal control will have an effect on the transport system. Previous studies have also shown that drivers may be slower to respond in hazardous situations with systems like adaptive cruise control (ACC) engaged, if the system suddenly fails. Little understanding has, so far, emerged to explain why. In this thesis, a situated approach to cognition was used to explain how the drivers’ goals and priorities might change with the opportunity of delegating control to a system. Additionally, drivers’ tactical driving decisions were studied to determine how a system such as ACC is integrated into driving. Such changes could, possibly, impact measures like response times as well. The research focused on how drivers handle the addition of ACC to their drive, especially when managing traffic conflicts. Traffic conflicts are not rare events, but form part of everyday driving. Three methods were used: A questionnaire addressing drivers’ understanding of system limitations, a simulator study on handling traffic conflicts when using ACC, and a database study of a field operational test comparing driver responses with and without ACC. The studies show that drivers do take system behaviour into account when handling traffic conflicts, sometimes allowing the system to act, sometimes by resuming manual control themselves. Previous experience with ACC was also found to affect not only drivers’ knowledge of system limitations, but also their response time to unwanted system behaviour. Drivers were, as previously found, slower to respond with automation than without in simulated driving. In contrast, when studying driver response times in the field test, drivers were faster to respond to a cut-in situation with ACC active than without. The freedom of drivers to use the system as they wish may cause it to be used under different circumstances than in simulator studies, thus explaining the inconsistency with previous results. The results of this thesis indicate that tactical driving behaviour in common traffic situations is an important factor when discussing the effects of ACC and other advanced driver assistance systems. Thus, the cases in which driving is affected by the system are extended from system failures to a wide range of different situations. Merely using operational measures will miss this aspect, thus risking a depiction of driving with automation as more risky than it is. Rather, driving with automation needs to be studied from a tactical perspective, determining first how the systems are being used. Only then can the relevant operational measures be studied.

Subjects/Keywords: Infrastructure Engineering; Tactical driving behaviour; Driver experience; Field Operational Test; Active steering; Simulator; Driver behaviour

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APA (6^th Edition):

Larsson, A. (2013). Automation and the nature of driving - The effect of adaptive cruise control on drivers' tactical driving decisions. (Doctoral Dissertation). University of Lund. Retrieved from https://lup.lub.lu.se/record/4025423 ; https://portal.research.lu.se/ws/files/5375171/4025428.pdf

Chicago Manual of Style (16^th Edition):

Larsson, Annika. “Automation and the nature of driving - The effect of adaptive cruise control on drivers' tactical driving decisions.” 2013. Doctoral Dissertation, University of Lund. Accessed March 07, 2021. https://lup.lub.lu.se/record/4025423 ; https://portal.research.lu.se/ws/files/5375171/4025428.pdf.

MLA Handbook (7^th Edition):

Larsson, Annika. “Automation and the nature of driving - The effect of adaptive cruise control on drivers' tactical driving decisions.” 2013. Web. 07 Mar 2021.

Vancouver:

Larsson A. Automation and the nature of driving - The effect of adaptive cruise control on drivers' tactical driving decisions. [Internet] [Doctoral dissertation]. University of Lund; 2013. [cited 2021 Mar 07]. Available from: https://lup.lub.lu.se/record/4025423 ; https://portal.research.lu.se/ws/files/5375171/4025428.pdf.

Council of Science Editors:

Larsson A. Automation and the nature of driving - The effect of adaptive cruise control on drivers' tactical driving decisions. [Doctoral Dissertation]. University of Lund; 2013. Available from: https://lup.lub.lu.se/record/4025423 ; https://portal.research.lu.se/ws/files/5375171/4025428.pdf

Ohio University

6. Toussant, Erica A. Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts.

Degree: MS, Civil Engineering (Engineering and Technology), 2016, Ohio University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1451907184

► The number of roundabouts has increased substantially throughout the United States, but many people remain unfamiliar about their operations. This study was conducted to determine… (more)

Subjects/Keywords: Civil Engineering; roundabout; driver; behavior; VISSIM

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APA (6^th Edition):

Toussant, E. A. (2016). Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts. (Masters Thesis). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1451907184

Chicago Manual of Style (16^th Edition):

Toussant, Erica A. “Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts.” 2016. Masters Thesis, Ohio University. Accessed March 07, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1451907184.

MLA Handbook (7^th Edition):

Toussant, Erica A. “Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts.” 2016. Web. 07 Mar 2021.

Vancouver:

Toussant EA. Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts. [Internet] [Masters thesis]. Ohio University; 2016. [cited 2021 Mar 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1451907184.

Council of Science Editors:

Toussant EA. Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts. [Masters Thesis]. Ohio University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1451907184

Delft University of Technology

7. 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)

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

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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 Toronto

8. Stahl, Patrick. Defining, Investigating and Supporting Anticipatory Driving - A Systematic Investigation of the Competence to Predict Traffic.

Degree: PhD, 2015, University of Toronto

URL: http://hdl.handle.net/1807/79728

► Driving research shows safety and economic benefits of anticipatory competence. While the literature recognizes the notion of anticipation, it has not yet operationalized the construct.… (more)

▼ Driving research shows safety and economic benefits of anticipatory competence. While the literature recognizes the notion of anticipation, it has not yet operationalized the construct. This dissertation addresses this gap by presenting a systematic investigation of anticipation in driving. It starts by defining anticipation as a competence relying on the conscious perception of visual cues and their cognitive processing. Two driving simulator studies then investigate the ability of novice and experienced drivers to anticipate conflicts in stereotypical scenarios. The first experiment shows the feasibility of identifying anticipation through the surrogate measure of pre-event actions relative to a conflict event, and confirms the hypothesis that experienced drivers exhibit these actions more often. This experiment supports an information-processing model of anticipation. The model suggests two crucial steps for the facilitation of anticipatory competence: (1) the conscious perception of appropriate cues that serve as indicators for the traffic scenario, and (2) efficient cognitive processing of those cues for a correct situational assessment. The second experiment investigates the effect of two interfaces designed to aid these steps. The attentional interface aids only the perception of cues and is hypothesized to yield larger benefits for experienced drivers who do not struggle with the interpretation of traffic. The interpretational interface supports both steps and is hypothesized to improve particularly novice driversâ competence in interpreting traffic. Contrary to these hypotheses, results show similar improvements in anticipation for both interfaces across all participants, although fewer and shorter glances towards the attentional interface suggest that it is preferable from the perspective of driver distraction. This dissertation extends the formerly vague concept of anticipation by defining and operationalizing it as a construct, identifying anticipatory actions, demonstrating that driver experience predicts anticipatory competence, and suggesting aids to support anticipation. These advances promote anticipation as a viable construct for future driver behaviour research. Advisors/Committee Members: Donmez, Birsen, Jamieson, Greg A., Mechanical and Industrial Engineering.

Subjects/Keywords: Anticipation; Driver Behaviour; Driver Experience; Driving Simulator; In-Vehicle Displays; Novice Drivers; 0546

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APA (6^th Edition):

Stahl, P. (2015). Defining, Investigating and Supporting Anticipatory Driving - A Systematic Investigation of the Competence to Predict Traffic. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/79728

Chicago Manual of Style (16^th Edition):

Stahl, Patrick. “Defining, Investigating and Supporting Anticipatory Driving - A Systematic Investigation of the Competence to Predict Traffic.” 2015. Doctoral Dissertation, University of Toronto. Accessed March 07, 2021. http://hdl.handle.net/1807/79728.

MLA Handbook (7^th Edition):

Stahl, Patrick. “Defining, Investigating and Supporting Anticipatory Driving - A Systematic Investigation of the Competence to Predict Traffic.” 2015. Web. 07 Mar 2021.

Vancouver:

Stahl P. Defining, Investigating and Supporting Anticipatory Driving - A Systematic Investigation of the Competence to Predict Traffic. [Internet] [Doctoral dissertation]. University of Toronto; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1807/79728.

Council of Science Editors:

Stahl P. Defining, Investigating and Supporting Anticipatory Driving - A Systematic Investigation of the Competence to Predict Traffic. [Doctoral Dissertation]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/79728

New Jersey Institute of Technology

9. Yang, Jing. Driver behavior classification and lateral control for automobile safety systems.

Degree: PhD, Electrical and Computer Engineering, 2012, New Jersey Institute of Technology

URL: https://digitalcommons.njit.edu/dissertations/303

► Advanced driver assistance systems (ADAS) have been developed to help drivers maintain stability, improve road safety, and avoid potential collision. The data acquisition equipment… (more)

▼ Advanced driver assistance systems (ADAS) have been developed to help drivers maintain stability, improve road safety, and avoid potential collision. The data acquisition equipment that can be used to measure the state and parameter information of the vehicle may not be available for a standard passenger car due to economical and technical limitations. This work focuses on developing three technologies (longitudinal tire force estimation, driver behavior classification and lateral control) using low-cost sensors that can be utilized in ADAS. For the longitudinal tire force estimation, a low cost 1Hz positioning global system (GPS) and a steering angle sensor are used as the vehicle data acquisition equipment. A nonlinear extended two-wheel vehicle dynamic model is employed. The sideslip angle and the yaw rate are estimated by discrete Kalman Filter. A time independent piecewise optimization scheme is proposed to provide time-continuous estimates of longitude tire force, which can be transferred to the throttle/brake pedal position. The proposed method can be validated by the estimation results. Driver behavior classification systems can detect unsafe driver behavior and avoid potentially dangerous situations. To realize this strategy, a machine learning classification method, Gaussian Mixture model (GMM), is applied to classify driver behavior. In this application, a low cost 1Hz GPS receiver is considered as the vehicle data acquisition equipment instead of other more costly sensors (such as steering angle sensor, throttle/brake position sensor, and etc.). Since the driving information is limited, the nonlinear extended two-wheel vehicle dynamic model is adopted to reconstruct the driver behavior. Firstly, the sideslip angle and the yaw rate are calculated since they are not available from the GPS measurements. Secondly, a piecewise optimization scheme is proposed to reproduce the steering angle and the longitudinal force. Finally, a GMM classifier is trained to identify abnormal driver behavior. The simulation results demonstrated that the proposed scenario can detect the unsafe driver behavior effectively. The lateral control system developed in this study is a look-down reference system which uses a magnetic sensor at the front bumper to measure the front lateral displacement and a GPS to measure the vehicle's heading orientation. Firstly, the steering angles can be estimated by using the data provided by the front magnetic sensor and GPS. The estimation algorithm is an observer for a new extended single-track model, in which the steering angle and its derivative are viewed as two state variables. Secondly, the road curvature is determined based on the linear relationship with respect to the steering angle. Thirdly, an accurate and real-time estimation of the vehicle's lateral displacements can be accomplished according to a state observer. Finally, the closed loop controller is used as a compensator for automated steering. The proposed estimation and control algorithms are validated by… Advisors/Committee Members: Edwin Hou, MengChu Zhou, Nirwan Ansari.

Subjects/Keywords: Vehicle control; Lateral control; Driver behavior classification; Advanced driver assistance systems; Vehicle dynamics; Driver safety; Electrical and Electronics

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

APA (6^th Edition):

Yang, J. (2012). Driver behavior classification and lateral control for automobile safety systems. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/303

Chicago Manual of Style (16^th Edition):

Yang, Jing. “Driver behavior classification and lateral control for automobile safety systems.” 2012. Doctoral Dissertation, New Jersey Institute of Technology. Accessed March 07, 2021. https://digitalcommons.njit.edu/dissertations/303.

MLA Handbook (7^th Edition):

Yang, Jing. “Driver behavior classification and lateral control for automobile safety systems.” 2012. Web. 07 Mar 2021.

Vancouver:

Yang J. Driver behavior classification and lateral control for automobile safety systems. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2012. [cited 2021 Mar 07]. Available from: https://digitalcommons.njit.edu/dissertations/303.

Council of Science Editors:

Yang J. Driver behavior classification and lateral control for automobile safety systems. [Doctoral Dissertation]. New Jersey Institute of Technology; 2012. Available from: https://digitalcommons.njit.edu/dissertations/303

University of Michigan

10. 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 (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

University of Minnesota

11. Peterson, Colleen. The Decision To Speed In the United States – A Mixed Methods Study.

Degree: PhD, Epidemiology, 2020, University of Minnesota

URL: http://hdl.handle.net/11299/216118

► Speeding remains a major and consistent cause of U.S. roadway fatalities. The current research used a mixed methods approach to build a more comprehensive understanding… (more)

▼ Speeding remains a major and consistent cause of U.S. roadway fatalities. The current research used a mixed methods approach to build a more comprehensive understanding of which U.S. drivers decide to speed and why to inform novel speeding interventions. Data came from an online survey of a diverse group of drivers (N=309) from across the U.S. The survey collected information on participant demographics, driving history, behaviors, and related attitudes in the form of both open- and close-ended questions. The first manuscript identified qualitative themes from narratives explaining how and why participant speeding behaviors changed with age. Results show U.S. drivers often make deliberate choices to speed and do not consider speeding to be dangerous after achieving perceived driving mastery, but they tend to speed less due to family responsibility and prioritizing safety. The second manuscript featured latent class analysis resulting in four driver typologies representing: Externally Motivated Reactors, Non-Reactors, the Perceived Invulnerable, and the Perceived Vulnerable. Externally Motivated Reactors and Non-Reactors class members had the highest probability of extreme speeding, while Perceived Vulnerable class members endorsed a host of less risky driving responses. The third manuscript identified quantitative and qualitative commonalities and differences between minor, moderate, and extreme speeders. Speeders were most differentiated quantitatively by proportion of high risk and sensation-seeking personalities and qualitatively by the permanence and extent of speed reductions after crashes, speeding tickets, or driving with passengers. For all speeder types, considering oneself a good driver or not at-fault for a crash reduced intention to change speeding behaviors. These mixed methods results holistically describe a spectrum of U.S. drivers, their perceptions, attitudes, and contexts that lead to different speeding behaviors, and how these change with age. Findings show that effective means of encouraging U.S. drivers not to speed may be multi-pronged interventions encompassing environmental, social, and cognitive reframing approaches. Anti-speeding campaigns should target high sensation seekers, emphasize the safety of all roadway users, explain the connection between speed and safety, underscoring how speeding reduces driver control. Broad-based use of safe systems road design and expanded law enforcement strategies are also recommended.

Subjects/Keywords: driver attitude; driver behavior; latent class analysis; mixed methods; qualitative analysis; roadway safety

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APA (6^th Edition):

Peterson, C. (2020). The Decision To Speed In the United States – A Mixed Methods Study. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/216118

Chicago Manual of Style (16^th Edition):

Peterson, Colleen. “The Decision To Speed In the United States – A Mixed Methods Study.” 2020. Doctoral Dissertation, University of Minnesota. Accessed March 07, 2021. http://hdl.handle.net/11299/216118.

MLA Handbook (7^th Edition):

Peterson, Colleen. “The Decision To Speed In the United States – A Mixed Methods Study.” 2020. Web. 07 Mar 2021.

Vancouver:

Peterson C. The Decision To Speed In the United States – A Mixed Methods Study. [Internet] [Doctoral dissertation]. University of Minnesota; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11299/216118.

Council of Science Editors:

Peterson C. The Decision To Speed In the United States – A Mixed Methods Study. [Doctoral Dissertation]. University of Minnesota; 2020. Available from: http://hdl.handle.net/11299/216118

University of Toronto

12. He, Dengbo. Understanding and Supporting Anticipatory Driving in Automated Vehicles.

Degree: PhD, 2020, University of Toronto

URL: http://hdl.handle.net/1807/103691

► With state-of-the-art driving automation technology available to the public (i.e., SAE Level 2 driving automation), drivers no longer need to control the vehicle continuously, but… (more)

▼ With state-of-the-art driving automation technology available to the public (i.e., SAE Level 2 driving automation), drivers no longer need to control the vehicle continuously, but are still required to monitor the road and the automation, and take over control or adjust the automation’s setting when necessary. Thus, many driving skills, such as anticipatory driving, which can allow drivers to predict potential traffic changes and respond to them in advance, can still enhance driving safety in automated vehicles. Anticipatory driving has already been found to be more prevalent among experienced drivers in non-automated vehicles. However, the factors influencing anticipatory driving in automated vehicles has not yet been investigated. Thus, this dissertation aims to understand anticipatory driving behaviors in automated vehicles and investigate displays that can support it. Three driving simulator experiments were conducted. The first experiment investigated the relationships between anticipatory driving, distraction engagement, driving experience, and visual attention allocation in non-automated vehicles. The second experiment re-investigated the factors mentioned above in a simulated automated vehicle equipped with adaptive cruise control and lane keeping assistance. The third experiment investigated the effectiveness of two displays in supporting anticipatory driving among experienced and novice drivers in automated vehicles, i.e., a TORAC display with takeover request (TOR) and automation capability (AC) information, and a STTORAC display with surrounding traffic (ST) information in addition to the TORAC display. Results show that in both automated and non-automated vehicles, experienced drivers exhibited more anticipatory driving behaviors, and distraction engagement impeded anticipatory driving for both novice and experienced drivers. Further, allocating more visual attention toward cues indicating upcoming events increased the odds of exhibiting anticipatory driving behaviors in non-automated vehicles. For automated vehicles, it was found that drivers’ reliance on automation might have a larger impact on the performance of anticipatory driving compared with visual attention to cues. The TORAC display led to less anticipatory driving in automated vehicles, possibly because it led to over-reliance on automation. Providing additional context information in the STTORAC display presumably supported drivers’ anticipation of potential traffic conflicts. Advisors/Committee Members: Donmez, Birsen, Mechanical and Industrial Engineering.

Subjects/Keywords: Driver behavior; Driver distraction; Driving automation; Driving simulator; Experience; Visual attention; 0546

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

APA (6^th Edition):

He, D. (2020). Understanding and Supporting Anticipatory Driving in Automated Vehicles. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/103691

Chicago Manual of Style (16^th Edition):

He, Dengbo. “Understanding and Supporting Anticipatory Driving in Automated Vehicles.” 2020. Doctoral Dissertation, University of Toronto. Accessed March 07, 2021. http://hdl.handle.net/1807/103691.

MLA Handbook (7^th Edition):

He, Dengbo. “Understanding and Supporting Anticipatory Driving in Automated Vehicles.” 2020. Web. 07 Mar 2021.

Vancouver:

He D. Understanding and Supporting Anticipatory Driving in Automated Vehicles. [Internet] [Doctoral dissertation]. University of Toronto; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1807/103691.

Council of Science Editors:

He D. Understanding and Supporting Anticipatory Driving in Automated Vehicles. [Doctoral Dissertation]. University of Toronto; 2020. Available from: http://hdl.handle.net/1807/103691

University of Alberta

13. Luo,Ying. Asymmetric Driver Behaviour-Based Algorithms for Estimating Real-Time Freeway Operational Capacity.

Degree: MS, Department of Civil and Environmental Engineering, 2013, University of Alberta

URL: https://era.library.ualberta.ca/files/gx41mj85m

► To mitigate recurrent and non-recurrent congestion, and to make full use of limited roadway capacity, numerous Active Traffic Demand Management (ATDM) strategies have been proposed,… (more)

Subjects/Keywords: Asymmetric Driver Behaviour; Variable Speed Limit; Bottleneck capacity drop

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APA (6^th Edition):

Luo,Ying. (2013). Asymmetric Driver Behaviour-Based Algorithms for Estimating Real-Time Freeway Operational Capacity. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/gx41mj85m

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

Luo,Ying. “Asymmetric Driver Behaviour-Based Algorithms for Estimating Real-Time Freeway Operational Capacity.” 2013. Masters Thesis, University of Alberta. Accessed March 07, 2021. https://era.library.ualberta.ca/files/gx41mj85m.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

Luo,Ying. “Asymmetric Driver Behaviour-Based Algorithms for Estimating Real-Time Freeway Operational Capacity.” 2013. Web. 07 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Luo,Ying. Asymmetric Driver Behaviour-Based Algorithms for Estimating Real-Time Freeway Operational Capacity. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2021 Mar 07]. Available from: https://era.library.ualberta.ca/files/gx41mj85m.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Luo,Ying. Asymmetric Driver Behaviour-Based Algorithms for Estimating Real-Time Freeway Operational Capacity. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/gx41mj85m

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Linnaeus University

14. Thamel, Prasadini. Classification of Articulated hauler braking behaviours.

Degree: Mechanical Engineering, 2019, Linnaeus University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-95146

► This study is performed to identify the customer braking behaviors of Articulated haulers. The data files from the different customer sites are used to… (more)

Subjects/Keywords: Emergency braking; Driver braking behaviour; Active Hazard braking; Mechanical Engineering; Maskinteknik

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APA (6^th Edition):

Thamel, P. (2019). Classification of Articulated hauler braking behaviours. (Thesis). Linnaeus University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-95146

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):

Thamel, Prasadini. “Classification of Articulated hauler braking behaviours.” 2019. Thesis, Linnaeus University. Accessed March 07, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-95146.

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

MLA Handbook (7^th Edition):

Thamel, Prasadini. “Classification of Articulated hauler braking behaviours.” 2019. Web. 07 Mar 2021.

Vancouver:

Thamel P. Classification of Articulated hauler braking behaviours. [Internet] [Thesis]. Linnaeus University; 2019. [cited 2021 Mar 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-95146.

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

Council of Science Editors:

Thamel P. Classification of Articulated hauler braking behaviours. [Thesis]. Linnaeus University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-95146

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

15. Gouda, Satish Kumar. Driver and Traffic Impact on Battery Electric Vehicle Driving Range .

Degree: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper, 2020, Chalmers University of Technology

URL: http://hdl.handle.net/20.500.12380/302150

► The energy consumption and driving range of a Battery Electric Vehicle (BEV) is determined from tests on a chassis dynamometer under standardized conditions. However, when… (more)

Subjects/Keywords: Battery Electric Vehicle; Energy consumption, Traffic situation; driver behaviour; SUMO; CarMaker

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APA (6^th Edition):

Gouda, S. K. (2020). Driver and Traffic Impact on Battery Electric Vehicle Driving Range . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/302150

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):

Gouda, Satish Kumar. “Driver and Traffic Impact on Battery Electric Vehicle Driving Range .” 2020. Thesis, Chalmers University of Technology. Accessed March 07, 2021. http://hdl.handle.net/20.500.12380/302150.

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

MLA Handbook (7^th Edition):

Gouda, Satish Kumar. “Driver and Traffic Impact on Battery Electric Vehicle Driving Range .” 2020. Web. 07 Mar 2021.

Vancouver:

Gouda SK. Driver and Traffic Impact on Battery Electric Vehicle Driving Range . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/20.500.12380/302150.

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

Council of Science Editors:

Gouda SK. Driver and Traffic Impact on Battery Electric Vehicle Driving Range . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/302150

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

University of Cambridge

16. Skrypchuk, Lee. Investigating and Characterising the Impact of Vehicle Interfaces on Situation Awareness.

Degree: PhD, 2020, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/307921

► The modern-day vehicle hosts a complex environment where competing goals challenge the act of driving. Drivers may pursue Non-Driving Related Activities (NDRAs), resulting in multitasking.… (more)

▼ The modern-day vehicle hosts a complex environment where competing goals challenge the act of driving. Drivers may pursue Non-Driving Related Activities (NDRAs), resulting in multitasking. How NDRAs are achieved will depend upon whether In-Vehicle Information Systems (IVIS) are available. If not, alternative means may be taken that could compromise Driving Related Activity (DRA) performance. Situation Awareness (SA) is central to achieving multiple goals in a dynamic environment. This thesis investigates an Unconstrained approach to designing IVIS to examine how to improve multitasking performance in-vehicle through enhanced driver SA. A combination of theoretical and experimental activities were performed. A literature review in SA theory exposed a lack of detailed consideration for competing goals and task switching. A restated model of SA was proposed to show the impact on Situation Assessment and the Situation Model of the act of multitasking. Using a new methodological approach, a series of simulator experiments then aimed to measure and characterise the effect of increased awareness on task performance when multitasking. The results demonstrated that increased awareness improved task performance, therefore, providing scope for IVIS to achieve the same. Subsequently, by using the model proposed, IVIS concepts were designed to support either the NDRA or DRA, and a simulator experiment was performed. Supporting NDRAs using Contextual Cuing proved successful by reducing the cost of task switching, but impacted overtaking behaviour. Supporting the DRA, while successful for Lane-keeping, did not operate as intended. Drivers were not able to adequately use concurrent feedback due to not achieving a skilled level of behaviour. Finally, increased awareness impacted how a driver manages their attention. Drivers acted more strategically, showing evidence for dynamic compensatory metacognition. Therefore, by using SA to understand the vehicle environment, in detail, and taking an Unconstrained approach to IVIS design, improved in-vehicle multitasking performance can be achieved.

Subjects/Keywords: Situation Awareness; Human Machine Interface; Driver Behaviour; System Design; Automotive

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

APA (6^th Edition):

Skrypchuk, L. (2020). Investigating and Characterising the Impact of Vehicle Interfaces on Situation Awareness. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/307921

Chicago Manual of Style (16^th Edition):

Skrypchuk, Lee. “Investigating and Characterising the Impact of Vehicle Interfaces on Situation Awareness.” 2020. Doctoral Dissertation, University of Cambridge. Accessed March 07, 2021. https://www.repository.cam.ac.uk/handle/1810/307921.

MLA Handbook (7^th Edition):

Skrypchuk, Lee. “Investigating and Characterising the Impact of Vehicle Interfaces on Situation Awareness.” 2020. Web. 07 Mar 2021.

Vancouver:

Skrypchuk L. Investigating and Characterising the Impact of Vehicle Interfaces on Situation Awareness. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Mar 07]. Available from: https://www.repository.cam.ac.uk/handle/1810/307921.

Council of Science Editors:

Skrypchuk L. Investigating and Characterising the Impact of Vehicle Interfaces on Situation Awareness. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/307921

University of Helsinki

17. Mattsson, Markus. The measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland.

Degree: Institute of Behavioural Sciences; Helsingfors universitet, Beteendevetenskapliga fakulteten, Institutionen för beteendevetenskaper, 2015, University of Helsinki

URL: http://hdl.handle.net/10138/154955

► Tässä pro gradu –työssä tarkastellaan liikennepsykologiassa ehkä eniten käytetyn kyselyinstrumentin, Driver Behaviour Questionnaire DBQ:n mittausinvarianssia suomalaisten ja irlantilaisten nuorten kuljettajien (18–25 v.) otoksissa. DBQ on… (more)

▼ Tässä pro gradu –työssä tarkastellaan liikennepsykologiassa ehkä eniten käytetyn kyselyinstrumentin, Driver Behaviour Questionnaire DBQ:n mittausinvarianssia suomalaisten ja irlantilaisten nuorten kuljettajien (18–25 v.) otoksissa. DBQ on kehitetty 1990-luvun alussa pääkomponenttianalyysin perusteella. Kysely perustui alun perin laajalti testattuun kognitiivisen ergonomian teoriaan (Generic Error Modeling System, GEMS), mutta sittemmin kyselyn osioiden joukko ja sen faktori- tai pääkomponenttirakenne ovat määräytyneet aineistolähtöisesti. Tämä on johtanut siihen, että kyselystä on olemassa runsaasti erilaisia versioita, joiden osiomäärä vaihtelee yhdeksästä yli sataan ja faktorimäärä yhdestä seitsemään. Kyselyn perusteella lasketaan yleisesti summa- tai keskiarvomuuttujia, joita vertaillaan vastaajien osajoukoissa. Mahdollisesti yleisimmin kyselystä käytetään 28 osion versiota, jonka ajatellaan mittaavan kahta, kolmea tai neljää latenttia muuttujaa. Tässä tutkimuksessa tarkastellaan konfirmatorisen faktorianalyysin ja erityisesti mittausinvarianssianalyysin keinoin, mikä esitetyistä faktorirakenteista sopii kuvaamaan suomalaisten ja irlantilaisten nuorten kuljettajien vastauksia parhaiten. Mittausinvarianssianalyysi perustuu ajatukseen siitä, että aineistoon sovitetaan sarja malleja, joissa rajoitetaan koko ajan kasvava joukko mallin parametreja identtisiksi vertailtavien otosten välillä. Mikäli rajoitettu malli ei sovi tilastollisessa mielessä rajoittamatonta mallia huonommin aineistoon, voidaan rajoitettua mallia soveltaa kaikkiin (tässä: molempiin) vertailtaviin aineistoihin. Aineistoon sovitettavat mallit ovat järjestyksessä: 1) mittausmalli, jossa ainoastaan faktoreiden määrä on rajoitettu, 2) heikon invarianssin malli, jossa faktorilataukset on rajoitettu samoiksi, 3) vahvan invarianssin malli, jossa lisäksi osioiden vakiotermit on rajoitettu samoiksi ja 4) tiukan invarianssin malli, jossa lisäksi osioiden virhetermit on rajoitettu samoiksi. Lisäksi sovelletaan osittaisen invarianssin malleja, joissa vain osa kyseisessä invarianssitestauksen vaiheessa asetettavista rajoituksista pidetään voimassa. Mallien tilastollisen vertailun lisäksi niiden sopivuutta aineistoon arvioidaan erilaisten kuvailevien tunnuslukujen ja graafisten esitysten avulla. Keskeisenä tuloksena esitetään, että vertailluista malleista neljän faktorin malli sopii parhaiten molempiin aineistoihin, vaikka mallia onkin muokattava aineistolähtöisesti, jotta riittävä yhteensopivuus aineiston kanssa saadaan varmistettua.Tarkempi tarkastelu osoittaa, että neljästä faktorista kaksi ovat luonteeltaan erilaisia kahdessa otoksessa, sillä vain irlantilaisessa otoksessa kaikki osiot latautuvat odotusten mukaisille faktoreille. Toisaalta kahden muun faktorin analyysi osoittaa, että niille latautuvat osiot tulkitaan olennaisesti samalla tavoin kahdessa otoksessa ja heikon invarianssin oletus voidaan tehdä. Lisäksi voidaan tehdä osittaisen vahvan invarianssin oletus yhden faktorin tapauksessa, vaikka tällöinkin suurin osa faktorille latautuvien…

Subjects/Keywords: Driver Behavior Questionnaire; DBQ; konfirmatorinen faktorianalyysi; CFA; mittausinvarianssianalyysi; Psychology; Psykologia; Psykologi; Driver Behavior Questionnaire; DBQ; konfirmatorinen faktorianalyysi; CFA; mittausinvarianssianalyysi

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

APA (6^th Edition):

Mattsson, M. (2015). The measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland. (Masters Thesis). University of Helsinki. Retrieved from http://hdl.handle.net/10138/154955

Chicago Manual of Style (16^th Edition):

Mattsson, Markus. “The measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland.” 2015. Masters Thesis, University of Helsinki. Accessed March 07, 2021. http://hdl.handle.net/10138/154955.

MLA Handbook (7^th Edition):

Mattsson, Markus. “The measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland.” 2015. Web. 07 Mar 2021.

Vancouver:

Mattsson M. The measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland. [Internet] [Masters thesis]. University of Helsinki; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10138/154955.

Council of Science Editors:

Mattsson M. The measurement invariance of the Driver Behavior Questionnaire across samples of young drivers from Finland and Ireland. [Masters Thesis]. University of Helsinki; 2015. Available from: http://hdl.handle.net/10138/154955

Loughborough University

18. Macdonald-Ames, Sandra. How do emergency vehicle markings and warning systems influence the interaction between emergency and civilian drivers?.

Degree: PhD, 2020, Loughborough University

URL: https://doi.org/10.26174/thesis.lboro.12811739.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820005

► AIM. The primary aim of the research presented in this thesis was to establish if emergency vehicle markings and warning systems influenced the way in… (more)

▼ AIM. The primary aim of the research presented in this thesis was to establish if emergency vehicle markings and warning systems influenced the way in which either a civilian or emergency driver responded when interacting during emergency driving situations. This was achieved by utilising a variety of research methods and a wide range of data types, including self-report questionnaires, Police collision reports, and real-world video data. The intention was that the findings could be used to inform approaches towards improving the on-road interaction between civilian and emergency drivers. BACKGROUND. Numerous emergency vehicle interactions occur without incident, yet some result in near misses, and collisions – both minor and serious in nature. Previous research (Shultz et al. 2009) has reported that civilian drivers often act in an adverse manner such as a panicked reaction (Gormley et al. 2009), due to poor vehicle salience, or modern vehicle soundproofing and technology distractions, when interacting with a responding emergency vehicle. Consequences of these negative interactions include feelings of frustration by the emergency driver, blame apportionment, and financial and reputational damage to the organisations themselves. Following an extensive review of the literature, research therefore firstly established the opinions of emergency and civilian drivers. Subsequent analysis of data, involving use of both marked and unmarked Police vehicles then helped to establish whether near misses and collisions occur as a result of marking type, through poor conspicuity (salience and warning systems) or as a result of behavioural change in the drivers themselves. METHODS. The research was conducted through four studies, using a multi-methods approach, to establish i.) The attitudes and opinions of emergency service drivers towards the public through questionnaire survey. ii.) A comparison between both marked and unmarked Police vehicle collision data and the effect of emergency warning systems on collision liability, over a 4.5 year time frame utilising telematics data from both vehicle marking types. iii.) Analysis of Police real world video footage observing the interaction between a civilian driver and a responding Police vehicle. iv.) Civilian drivers' perceptions of how they interact with the emergency vehicle when allowing for its presence on the road through questionnaire survey. RESULTS. Important findings identified through self report questionnaires showed that Police drivers believed they were the least aggressive drivers, in comparison to their emergency service driver peers. Ambulance drivers were the most frustrated with other road users but were more willing to discuss their feelings, whilst Fire Service drivers were more likely to take risks in order to arrive more quickly at an emergency situation. Evidence gathered and reviewed showed that the public reacted in two distinct ways when giving ease of passage to an emergency vehicle. On high speed roads, civilian drivers showed an initial delayed reaction,…

Subjects/Keywords: Police driving; Driver Behaviour; Driver panic; Blue Light; Emergency driving; Emergency services; Fire Service driving; Ambulance driving

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APA (6^th Edition):

Macdonald-Ames, S. (2020). How do emergency vehicle markings and warning systems influence the interaction between emergency and civilian drivers?. (Doctoral Dissertation). Loughborough University. Retrieved from https://doi.org/10.26174/thesis.lboro.12811739.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820005

Chicago Manual of Style (16^th Edition):

Macdonald-Ames, Sandra. “How do emergency vehicle markings and warning systems influence the interaction between emergency and civilian drivers?.” 2020. Doctoral Dissertation, Loughborough University. Accessed March 07, 2021. https://doi.org/10.26174/thesis.lboro.12811739.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820005.

MLA Handbook (7^th Edition):

Macdonald-Ames, Sandra. “How do emergency vehicle markings and warning systems influence the interaction between emergency and civilian drivers?.” 2020. Web. 07 Mar 2021.

Vancouver:

Macdonald-Ames S. How do emergency vehicle markings and warning systems influence the interaction between emergency and civilian drivers?. [Internet] [Doctoral dissertation]. Loughborough University; 2020. [cited 2021 Mar 07]. Available from: https://doi.org/10.26174/thesis.lboro.12811739.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820005.

Council of Science Editors:

Macdonald-Ames S. How do emergency vehicle markings and warning systems influence the interaction between emergency and civilian drivers?. [Doctoral Dissertation]. Loughborough University; 2020. Available from: https://doi.org/10.26174/thesis.lboro.12811739.v1 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.820005

University of Utah

19. Taylor, Jeffrey D. Computational methods for investigating intradriver heterogeneity using vehicle trajectory data.

Degree: MS, Civil & Environmental Engineering, 2014, University of Utah

URL: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3159/rec/527

► Traffic simulations, which attempt to describe how individual vehicles move on road segments in a network, rely on mathematical traffic flow models developed from empirical… (more)

Subjects/Keywords: Car following model; Driver behavior; Dynamic time warping; Heterogeneity; NGSIM

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APA (6^th Edition):

Taylor, J. D. (2014). Computational methods for investigating intradriver heterogeneity using vehicle trajectory data. (Masters Thesis). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3159/rec/527

Chicago Manual of Style (16^th Edition):

Taylor, Jeffrey D. “Computational methods for investigating intradriver heterogeneity using vehicle trajectory data.” 2014. Masters Thesis, University of Utah. Accessed March 07, 2021. http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3159/rec/527.

MLA Handbook (7^th Edition):

Taylor, Jeffrey D. “Computational methods for investigating intradriver heterogeneity using vehicle trajectory data.” 2014. Web. 07 Mar 2021.

Vancouver:

Taylor JD. Computational methods for investigating intradriver heterogeneity using vehicle trajectory data. [Internet] [Masters thesis]. University of Utah; 2014. [cited 2021 Mar 07]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3159/rec/527.

Council of Science Editors:

Taylor JD. Computational methods for investigating intradriver heterogeneity using vehicle trajectory data. [Masters Thesis]. University of Utah; 2014. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3159/rec/527

Texas A&M University

20. Zhou, Zheren. Modeling and Verification of Naturalistic Lane Keeping System.

Degree: MS, Mechanical Engineering, 2016, Texas A&M University

URL: http://hdl.handle.net/1969.1/158128

► In order to lower human drivers’ driving load and to enhance their systematic performance during driving, driver assistant systems have been introduced during the past… (more)

Subjects/Keywords: Vehicle; Dynamic; Control; Lane keeping; Driver; Driving behavior; Naturalistic

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APA (6^th Edition):

Zhou, Z. (2016). Modeling and Verification of Naturalistic Lane Keeping System. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/158128

Chicago Manual of Style (16^th Edition):

Zhou, Zheren. “Modeling and Verification of Naturalistic Lane Keeping System.” 2016. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/158128.

MLA Handbook (7^th Edition):

Zhou, Zheren. “Modeling and Verification of Naturalistic Lane Keeping System.” 2016. Web. 07 Mar 2021.

Vancouver:

Zhou Z. Modeling and Verification of Naturalistic Lane Keeping System. [Internet] [Masters thesis]. Texas A&M University; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/158128.

Council of Science Editors:

Zhou Z. Modeling and Verification of Naturalistic Lane Keeping System. [Masters Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/158128

Iowa State University

21. Kirsch, Trevor Joseph. An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data.

Degree: 2018, Iowa State University

URL: https://lib.dr.iastate.edu/etd/16392

► Distracted driving has become a severe threat to traffic safety due in large part to the proliferation of in-vehicle smart technologies, the ubiquity of cell… (more)

▼ Distracted driving has become a severe threat to traffic safety due in large part to the proliferation of in-vehicle smart technologies, the ubiquity of cell phones, and a general societal shift towards constant mobility and connectivity. Research has consistently demonstrated adverse consequences to engaging in a distracting secondary behavior while operating a motor vehicle. Much of the prior research in this area has leveraged data from traffic simulators and police-reported crash data, resulting in estimates as to the impacts of distraction on crash risk. However, research has been more limited under actual driving conditions and there remain important gaps with respect to how distracted driving and the associated crash risks vary across drivers and roadway environments. This study addresses this gap by utilizing disaggregate time-series data from the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) to conduct an in-depth investigation of various safety-focused aspects of distracted driving. The high resolution data were provided at 10 Hz resolution through a series of cameras and mechanical sensors. These operational data were integrated with geometric information from the companion Roadway Information Database (RID), as well as with data related to driver behavioral characteristics, risk perceptions, and risk-taking behavior from a series of participant surveys. Collectively, these sources resulted in a robust dataset of vehicle, roadway, weather, and driver behavioral parameters. Various aspects of distracted driving were investigated as a part of this analysis, including the effects of distraction on driving performance. More specifically, the effects of various types of distraction on driver speed selection behavior was examined. Additional analyses assessed how the prevalence of various types of distracting behaviors varied based upon driver characteristics, roadway geometry, traffic conditions, and environmental conditions. As a part of these investigations, a series of random effects linear and logistic regression models were estimated with the disaggregate time-series information. Risk models were also estimated to determine how various types of distractions impacted the likelihood of a crash or near-crash event. Ultimately, the results suggest that drivers generally adapt their behavior based upon the level of risk posed by various driving environments. These environmental factors, along with various driver-specific factors, were shown to influence speed selection, as well as proclivity for participating in various types of distracting behaviors. In turn, these distractions were found to exacerbate crash risks, with marked differences exhibited based upon the degree to which the distracting behaviors required drivers to direct their attention away from the primary driving task.

Subjects/Keywords: crash risk; distracted; driver behavior; roadway geometrics; safety; time-series; Transportation

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APA (6^th Edition):

Kirsch, T. J. (2018). An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16392

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):

Kirsch, Trevor Joseph. “An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data.” 2018. Thesis, Iowa State University. Accessed March 07, 2021. https://lib.dr.iastate.edu/etd/16392.

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

MLA Handbook (7^th Edition):

Kirsch, Trevor Joseph. “An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data.” 2018. Web. 07 Mar 2021.

Vancouver:

Kirsch TJ. An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data. [Internet] [Thesis]. Iowa State University; 2018. [cited 2021 Mar 07]. Available from: https://lib.dr.iastate.edu/etd/16392.

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

Council of Science Editors:

Kirsch TJ. An analysis of the crash risk and likelihood of engaging in a distraction while driving using naturalistic, time-series data. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16392

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

22. Negi, N.S. (author). Differences in steering behaviour between experts, experienced and novice drivers: A driving simulator study.

Degree: 2013, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:eaf3000c-13dd-4c97-aa2c-3cf0a016a3c1

►

Through the years of automotive development driving safety has been one of the primary areas of concern. Inappropriate driver behaviour and insufficient driving skill are… (more)

▼

Through the years of automotive development driving safety has been one of the primary areas of concern. Inappropriate driver behaviour and insufficient driving skill are considered the primary causes of road accidents. Advanced driver assist systems are becoming increasingly important in their role of increasing driver safety. However, these technological advancements are not driver specific and often benchmark the average driver performance. A solution is to gain knowledge of the differences in driver skill and their relation to driver performance. In this thesis the steering behaviour of novice, experienced and expert drivers is investigated. Three experiments were analysed to research the differences between these drivers on a demanding racetrack, a double lane change and in a high and low friction cornering manoeuvre. In the first experiment expert drivers showed increased steering activity and differences in path strategy compared to normal drivers to achieve their better performance (faster lap-times and higher average lateral acceleration). Participants with more driving experience achieved better performance in the double lane change task (number of cones hit and deviation from the mid-path) and also in the cornering manoeuvre (faster lap-times and higher average lateral acceleration). Significant differences were found in driver control actions, showing higher steering activity in terms of steering rate, steering jerk and steering reversals for the expert and experienced drivers for the cornering tasks. Furthermore, differences were found in the path strategy and path consistency between the experts and normal drivers. In the double lane change test, novices show incorrect timing of the control action and hence provide late initial steering input and try to over compensate in a later stage, resulting in poor performance in terms of deviation from the mid-path and the number of cones hit. These results confirm previous findings in literature that increased control activity can lead to better performance and may lead to a future classification system of drivers based on their steering behaviour.

Automotive

Precision and Microsystems Engineering

Mechanical, Maritime and Materials Engineering

Advisors/Committee Members: Holweg, E.G.M. (mentor), Happee, R. (mentor), Van Leeuwen, P. (mentor).

Subjects/Keywords: steering behavior; driver simulator

…the differences in driver skill level and variations in driver control behavior and use this… …aims at focusing on driver behavior in conditions involving extreme steering and loss of… …driver behavior during driving tasks like lane change and cornering. Performance of novice… …tasks. In the context of driving behavior, a novice driver is one who is familiar with the… …x28;2007), line jump scenario to investigate driver steering behavior during sudden…

10 more images…

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APA (6^th Edition):

Negi, N. S. (. (2013). Differences in steering behaviour between experts, experienced and novice drivers: A driving simulator study. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:eaf3000c-13dd-4c97-aa2c-3cf0a016a3c1

Chicago Manual of Style (16^th Edition):

Negi, N S (author). “Differences in steering behaviour between experts, experienced and novice drivers: A driving simulator study.” 2013. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:eaf3000c-13dd-4c97-aa2c-3cf0a016a3c1.

MLA Handbook (7^th Edition):

Negi, N S (author). “Differences in steering behaviour between experts, experienced and novice drivers: A driving simulator study.” 2013. Web. 07 Mar 2021.

Vancouver:

Negi NS(. Differences in steering behaviour between experts, experienced and novice drivers: A driving simulator study. [Internet] [Masters thesis]. Delft University of Technology; 2013. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:eaf3000c-13dd-4c97-aa2c-3cf0a016a3c1.

Council of Science Editors:

Negi NS(. Differences in steering behaviour between experts, experienced and novice drivers: A driving simulator study. [Masters Thesis]. Delft University of Technology; 2013. Available from: http://resolver.tudelft.nl/uuid:eaf3000c-13dd-4c97-aa2c-3cf0a016a3c1

East Tennessee State University

23. Sims, Brian K. Driving and Thriving: School Bus Drivers and the Behavior Management Strategies They Use.

Degree: EdD (Doctor of Education), Educational Leadership, 2014, East Tennessee State University

URL: https://dc.etsu.edu/etd/2423

► The purpose of this study was first to determine the satisfaction level of bus drivers pertaining to school building administration, transportation department, and student… (more)

▼ The purpose of this study was first to determine the satisfaction level of bus drivers pertaining to school building administration, transportation department, and student behaviors, and second to identify the common behavior management strategies used by bus driver in a particular school system in east Tennessee. I also compared the common behavior management strategies used by school bus drivers who are also employed by the school system in some position in addition to this vocation with school bus drivers who are not employed by the school system other than driving the school bus. I also compared behavior management strategies in the following categories: age, years of experience, and gender. For this quantitative element of the study, I requested bus drivers who met the criteria complete an anonymous survey. The survey had 20 items that focus on the bus drivers' satisfaction in areas of school building administration, transportation department, and student behavior. Bus drivers responded to each item by selecting responses on a 5-point scale from extremely dissatisfied to extremely satisfied, with neutral being the middle point. A single sample t-test was conducted and the results showed bus drivers were satisfied to a significant extent with school building administration and transportation department, while bus drivers were neither satisfied or dissatisfied with student behavior. Bus drivers also ranked their top five behavior management strategies. Results were categorized by age, years of experience, gender, and whether they were employed by the school system in another position. The overall top five behavior management strategies by bus drivers were 1) Assigning a student to a particular seat, 2) Reporting students to school building administration, 3) Moving a student to a particular seat during the bus route, 4) Use of video surveillance, and 5) Discussing a student's behavior with a parent or guardian. I also interviewed 10 school building administrators in the same school system for their perspective on student behavior management strategies recommended for bus drivers to use on school buses and also their perspective on the impact student behavior on a school bus has on a student at school. Responses were also solicited from school building administrators of their perspective of driver management practices that seem most and least conducive to managing and preventing behaviors on buses. I recorded the responses given to these questions and listed the responses along with any additional comments from administrators. Most of the responses correspond with the responses bus drivers gave in their interviews. Half of the administrators stated school buses should be operated like a classroom with rules and consequences.

Subjects/Keywords: bus driver; student transportation; behavior management strategy; school bus; Educational Leadership

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APA (6^th Edition):

Sims, B. K. (2014). Driving and Thriving: School Bus Drivers and the Behavior Management Strategies They Use. (Thesis). East Tennessee State University. Retrieved from https://dc.etsu.edu/etd/2423

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):

Sims, Brian K. “Driving and Thriving: School Bus Drivers and the Behavior Management Strategies They Use.” 2014. Thesis, East Tennessee State University. Accessed March 07, 2021. https://dc.etsu.edu/etd/2423.

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

MLA Handbook (7^th Edition):

Sims, Brian K. “Driving and Thriving: School Bus Drivers and the Behavior Management Strategies They Use.” 2014. Web. 07 Mar 2021.

Vancouver:

Sims BK. Driving and Thriving: School Bus Drivers and the Behavior Management Strategies They Use. [Internet] [Thesis]. East Tennessee State University; 2014. [cited 2021 Mar 07]. Available from: https://dc.etsu.edu/etd/2423.

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

Council of Science Editors:

Sims BK. Driving and Thriving: School Bus Drivers and the Behavior Management Strategies They Use. [Thesis]. East Tennessee State University; 2014. Available from: https://dc.etsu.edu/etd/2423

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

University of Minnesota

24. Chatterjee, Indrajit. Understanding Driver Contributions to Rear-End Crashes on Congested Freeways and their Implications for Future Safety Measures.

Degree: PhD, Civil Engineering, 2016, University of Minnesota

URL: http://hdl.handle.net/11299/181704

► Empirical evidence for occurrences of shockwaves as a primary cause of rear-end crashes on freeways is well documented in the transportation safety literature. However, existing… (more)

Subjects/Keywords: autonomous vehicles; crash; driver behavior; liability policy; rear-end

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APA (6^th Edition):

Chatterjee, I. (2016). Understanding Driver Contributions to Rear-End Crashes on Congested Freeways and their Implications for Future Safety Measures. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/181704

Chicago Manual of Style (16^th Edition):

Chatterjee, Indrajit. “Understanding Driver Contributions to Rear-End Crashes on Congested Freeways and their Implications for Future Safety Measures.” 2016. Doctoral Dissertation, University of Minnesota. Accessed March 07, 2021. http://hdl.handle.net/11299/181704.

MLA Handbook (7^th Edition):

Chatterjee, Indrajit. “Understanding Driver Contributions to Rear-End Crashes on Congested Freeways and their Implications for Future Safety Measures.” 2016. Web. 07 Mar 2021.

Vancouver:

Chatterjee I. Understanding Driver Contributions to Rear-End Crashes on Congested Freeways and their Implications for Future Safety Measures. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11299/181704.

Council of Science Editors:

Chatterjee I. Understanding Driver Contributions to Rear-End Crashes on Congested Freeways and their Implications for Future Safety Measures. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/181704

University of Tennessee – Knoxville

25. Bartnik, Bryan Andre. Driver Behavior at Railway-Highway Grade Crossings with Passive Traffic Control: A Driving Simulator Study.

Degree: MS, Civil Engineering, 2013, University of Tennessee – Knoxville

URL: https://trace.tennessee.edu/utk_gradthes/2387

► Research to evaluate driver behavior at railway-highway grade crossings with passive traffic control attempts to find an answer to a much debated subject. This… (more)

Subjects/Keywords: Driver Behavior Passive Grade Crossing Simulator; Civil Engineering

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APA (6^th Edition):

Bartnik, B. A. (2013). Driver Behavior at Railway-Highway Grade Crossings with Passive Traffic Control: A Driving Simulator Study. (Thesis). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_gradthes/2387

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):

Bartnik, Bryan Andre. “Driver Behavior at Railway-Highway Grade Crossings with Passive Traffic Control: A Driving Simulator Study.” 2013. Thesis, University of Tennessee – Knoxville. Accessed March 07, 2021. https://trace.tennessee.edu/utk_gradthes/2387.

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

MLA Handbook (7^th Edition):

Bartnik, Bryan Andre. “Driver Behavior at Railway-Highway Grade Crossings with Passive Traffic Control: A Driving Simulator Study.” 2013. Web. 07 Mar 2021.

Vancouver:

Bartnik BA. Driver Behavior at Railway-Highway Grade Crossings with Passive Traffic Control: A Driving Simulator Study. [Internet] [Thesis]. University of Tennessee – Knoxville; 2013. [cited 2021 Mar 07]. Available from: https://trace.tennessee.edu/utk_gradthes/2387.

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

Council of Science Editors:

Bartnik BA. Driver Behavior at Railway-Highway Grade Crossings with Passive Traffic Control: A Driving Simulator Study. [Thesis]. University of Tennessee – Knoxville; 2013. Available from: https://trace.tennessee.edu/utk_gradthes/2387

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

26. Tupper, Steven Maxwell. Safety and Operational Assessment of Gap Acceptance Through Large-Scale Field Evaluation.

Degree: MS, Civil Engineering, 2011, University of Massachusetts

URL: https://scholarworks.umass.edu/theses/651

► Given that “driver error” is cited as a contributing factor in 93 percent of all crashes, understanding driver behavior is an essential element in… (more)

▼ Given that “driver error” is cited as a contributing factor in 93 percent of all crashes, understanding driver behavior is an essential element in mitigating the crash problem. Among the more dangerous roadway elements are unsignalized intersections where drivers’ gap acceptance behavior is strongly correlated to the operational and safety performance of the intersection. While a basic understanding of drivers’ gap acceptance behavior exists, several unanswered questions remain. Previous work has attempted to address some of these questions, however to date the research has been somewhat limited in scope and scale due to the challenges of collecting high fidelity gap acceptance data in the field. This research initiative utilized software newly developed for this project to collect gap acceptance data on 2,767 drivers at 60 sites, totaling 10,419 driver decisions and 22,639 gaps in traffic. This large-scale data collection effort allowed many of these remaining questions to be answered with an improved degree of certainty. This research initiative showed that naturalistic driver gap acceptance behavior can realistically be observed and accurately recorded in the field in real time using a newly developed software tool. This software tool and study methodology was validation using high fidelity video reduction techniques. This research compared different methods of analyzing gap acceptance data, in particular determining critical gap, seeing that the method used significantly affects the results. Conclusions were draw about the merits of each of the ten analysis methods considered. Through the analysis of the large data set collected, the research determined that there exist appreciable and identifiable differences in gap acceptance behavior across drivers under varied conditions. The greatest differences were seen in relationship to wait time and queue presence. If a driver has queued vehicles waiting behind them and/or has been waiting to turn for a long period of time, they will be more likely to accept a smaller gap in traffic. Additionally, an analysis of gap acceptance as it relates to crash experience identified critical situations where a driver's gap acceptance behavior contributes to the occurrence of a crash. Characteristics of the driver such as gender and approximate age associated with specific crashes were examined. Teen drivers were identified as exhibiting aggressive gap acceptance behavior and were found to be overrepresented in gap acceptance related crashes. Ultimately, a better understanding of the driver and environmental factors that significantly contribute to increased crash risk will help guide the way to targeted design solutions. Advisors/Committee Members: Michael A. Knodler.

Subjects/Keywords: gap acceptance; critical gap; gap; driver behavior; traffic safety; Civil Engineering

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APA (6^th Edition):

Tupper, S. M. (2011). Safety and Operational Assessment of Gap Acceptance Through Large-Scale Field Evaluation. (Masters Thesis). University of Massachusetts. Retrieved from https://scholarworks.umass.edu/theses/651

Chicago Manual of Style (16^th Edition):

Tupper, Steven Maxwell. “Safety and Operational Assessment of Gap Acceptance Through Large-Scale Field Evaluation.” 2011. Masters Thesis, University of Massachusetts. Accessed March 07, 2021. https://scholarworks.umass.edu/theses/651.

MLA Handbook (7^th Edition):

Tupper, Steven Maxwell. “Safety and Operational Assessment of Gap Acceptance Through Large-Scale Field Evaluation.” 2011. Web. 07 Mar 2021.

Vancouver:

Tupper SM. Safety and Operational Assessment of Gap Acceptance Through Large-Scale Field Evaluation. [Internet] [Masters thesis]. University of Massachusetts; 2011. [cited 2021 Mar 07]. Available from: https://scholarworks.umass.edu/theses/651.

Council of Science Editors:

Tupper SM. Safety and Operational Assessment of Gap Acceptance Through Large-Scale Field Evaluation. [Masters Thesis]. University of Massachusetts; 2011. Available from: https://scholarworks.umass.edu/theses/651

Virginia Tech

27. Tawfik, Aly M. Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior.

Degree: PhD, Civil Engineering, 2012, Virginia Tech

URL: http://hdl.handle.net/10919/37338

► Driver heterogeneity in travel behavior has repeatedly been cited in the literature as a limitation that needs to be addressed. In this work, driver heterogeneity… (more)

▼ Driver heterogeneity in travel behavior has repeatedly been cited in the literature as a limitation that needs to be addressed. In this work, driver heterogeneity is addressed from four different perspectives. First, driver heterogeneity is addressed by models of driver perceptions of travel conditions: travel distance, time, and speed. Second, it is addressed from the perspective of driver learning trends and models of driver-types. Driver type is not commonly used in the vernacular of transportation engineering. It is a term that was developed in this work to reflect driver aggressiveness in route switching behavior. It may be interpreted as analogous to the commonly known personality-types, but applied to driver behavior. Third, driver heterogeneity is addressed via latent class choice models. Last, personality traits were found significant in all estimated models. The first three adopted perspectives were modeled as functions of variables of driver demographics, personality traits, and choice situation characteristics. The work is based on three datasets: a driving simulator experiment, an in situ driving experiment in real-world conditions, and a naturalistic real-life driving experiment. In total, the results are based on three experiments, 109 drivers, 74 route choice situations, and 8,644 route choices. It is assuring that results from all three experiments were found to be highly consistent. Discrepancies between predictions of network-oriented traffic assignment models and observed route choice percentages were identified and incorporating variables of driver heterogeneity were found to improve route choice model performance. Variables from all three groups: driver demographics, personality traits, and choice situation characteristics, were found significant in all considered models for driver heterogeneity. However, it is extremely interesting that all five variables of driver personality traits were found to be, in general, as significant as, and frequently more significant than, variables of trip characteristics â such as travel time. Neuroticism, extraversion and conscientiousness were found to increase route switching behavior, and openness to experience and agreeable were found to decrease route switching behavior. In addition, as expected, travel time was found to be highly significant in the models that were developed. However, unexpectedly, travel speed was also found to be highly significant, and travel distance was not as significant as expected. Results of this work are highly promising for the future of understanding and modeling of heterogeneity of human travel behavior, as well as for identifying target markets and the future of intelligent transportation systems. Advisors/Committee Members: Rakha, Hesham A. (committeechair), Abbas, Montasir M. (committee member), Hobeika, Antoine G. (committee member), Kikuchi, Shinya (committee member), Smith-Jackson, Tonya L. (committee member).

Subjects/Keywords: Travel Behavior; Route Choice; Driver Heterogeneity; Human Factors; Personality Traits

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APA (6^th Edition):

Tawfik, A. M. (2012). Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/37338

Chicago Manual of Style (16^th Edition):

Tawfik, Aly M. “Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior.” 2012. Doctoral Dissertation, Virginia Tech. Accessed March 07, 2021. http://hdl.handle.net/10919/37338.

MLA Handbook (7^th Edition):

Tawfik, Aly M. “Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior.” 2012. Web. 07 Mar 2021.

Vancouver:

Tawfik AM. Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10919/37338.

Council of Science Editors:

Tawfik AM. Incorporating Perceptions, Learning Trends, Latent Classes, and Personality Traits in the Modeling of Driver Heterogeneity in Route Choice Behavior. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/37338

University of Washington

28. Li, Ning. Modeling driver behavior and their interactions with driver assistance systems.

Degree: PhD, 2019, University of Washington

URL: http://hdl.handle.net/1773/44835

► As vehicle automation becomes increasingly prevalent and capable, drivers have the opportunity to delegate primary driving task control to automated systems. In recent years, significant… (more)

▼ As vehicle automation becomes increasingly prevalent and capable, drivers have the opportunity to delegate primary driving task control to automated systems. In recent years, significant efforts have been placed on developing and deploying Advanced Driver Assistance Systems (ADAS). These systems are designed to work with human drivers to increase vehicle safety, control, and performance in both ordinary and emergent situations. Current ADAS are mainly presented in rule-based or manually programmed design based on the summary and modeling of pre-collected human performance data. However, the pre-fixed system with limited personalization may not match human drivers' needs, which may arise the driver's dissatisfaction and cause ineffective system improvement. Human-centered machine learning (HCML) includes explicitly recognizing this human operator's role, as well as re-constructing machine learning workflows based on human working practices. The goal of this dissertation is to build a novel driver behavior modeling framework to understand and predict interactions with the driver assistance system from a human-centered perspective. It can lead not only to more usable machine learning tools but to new ways of improving the driver assistance systems. A driving simulator study was conducted to evaluate drivers' interactions with Forward Collision Warning (FCW) system. Gaussian Mixture Model (GMM) clusterization was used to identify different driving styles based drivers' driving performance, secondary task engagement, eye glance behavior and survey information. The impact of the FCW system on the different driving styles was also evaluated and discussed from three perspectives: initial reaction, distraction types, and safety benefits. A driver behavior model was also built using inverse reinforcement learning. Lastly, the timing prediction of FCW using driving preference was compared to the algorithm from a traditional FCW system. The findings of this study showed that ADAS without human feedback may not always bring positive safety benefits. Learning driver's preference through inverse reinforcement learning could better account for future scenarios and better predict driver behavior (e.g., braking action). This algorithm can be incorporated into real world in-vehicle warning systems such that the feedback and driving styles of the human operator are appropriately considered. Advisors/Committee Members: Boyle, Linda N (advisor).

Subjects/Keywords: advanced driver assistance systems; behavioral adaptations; driver behavior; driving performance; reinforcement learning; statistics; Industrial engineering; Industrial engineering

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

APA (6^th Edition):

Li, N. (2019). Modeling driver behavior and their interactions with driver assistance systems. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/44835

Chicago Manual of Style (16^th Edition):

Li, Ning. “Modeling driver behavior and their interactions with driver assistance systems.” 2019. Doctoral Dissertation, University of Washington. Accessed March 07, 2021. http://hdl.handle.net/1773/44835.

MLA Handbook (7^th Edition):

Li, Ning. “Modeling driver behavior and their interactions with driver assistance systems.” 2019. Web. 07 Mar 2021.

Vancouver:

Li N. Modeling driver behavior and their interactions with driver assistance systems. [Internet] [Doctoral dissertation]. University of Washington; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1773/44835.

Council of Science Editors:

Li N. Modeling driver behavior and their interactions with driver assistance systems. [Doctoral Dissertation]. University of Washington; 2019. Available from: http://hdl.handle.net/1773/44835

University of Missouri – Columbia

29. Drum, David K. Counteracting traffic congestion using intelligent driver feedback.

Degree: 2014, University of Missouri – Columbia

URL: https://doi.org/10.32469/10355/44263

► Traffic congestion is a daily occurrence in urban highway networks worldwide. It is not possible, however, for society to build its way out of congestion;… (more)

Subjects/Keywords: Author supplied: driver behavior, machine learning, decision-support systems, traffic congestion, driver feedback, real-time data; Traffic congestion – Management; Artificial intelligence

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

APA (6^th Edition):

Drum, D. K. (2014). Counteracting traffic congestion using intelligent driver feedback. (Thesis). University of Missouri – Columbia. Retrieved from https://doi.org/10.32469/10355/44263

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):

Drum, David K. “Counteracting traffic congestion using intelligent driver feedback.” 2014. Thesis, University of Missouri – Columbia. Accessed March 07, 2021. https://doi.org/10.32469/10355/44263.

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

MLA Handbook (7^th Edition):

Drum, David K. “Counteracting traffic congestion using intelligent driver feedback.” 2014. Web. 07 Mar 2021.

Vancouver:

Drum DK. Counteracting traffic congestion using intelligent driver feedback. [Internet] [Thesis]. University of Missouri – Columbia; 2014. [cited 2021 Mar 07]. Available from: https://doi.org/10.32469/10355/44263.

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

Council of Science Editors:

Drum DK. Counteracting traffic congestion using intelligent driver feedback. [Thesis]. University of Missouri – Columbia; 2014. Available from: https://doi.org/10.32469/10355/44263

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

Michigan Technological University

30. Flanagan, Brian. ANOMALY DETECTION IN CONTROLLER AREA NETWORK DATA USING HIERARCHICAL ANALYSIS.

Degree: MS, Department of Electrical and Computer Engineering, 2018, Michigan Technological University

URL: https://digitalcommons.mtu.edu/etdr/576

► Recently, there has been a lot of attention given to autonomous vehicles and intelligent vehicular systems. Fully integrating intelligent systems into modern vehicles relies… (more)

Subjects/Keywords: Controller Area Network; Driver Behavior; Driver Model; Decision Forest; Hidden Markov Model; Artificial Intelligence and Robotics; Other Computer Engineering

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

APA (6^th Edition):

Flanagan, B. (2018). ANOMALY DETECTION IN CONTROLLER AREA NETWORK DATA USING HIERARCHICAL ANALYSIS. (Masters Thesis). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/576

Chicago Manual of Style (16^th Edition):

Flanagan, Brian. “ANOMALY DETECTION IN CONTROLLER AREA NETWORK DATA USING HIERARCHICAL ANALYSIS.” 2018. Masters Thesis, Michigan Technological University. Accessed March 07, 2021. https://digitalcommons.mtu.edu/etdr/576.

MLA Handbook (7^th Edition):

Flanagan, Brian. “ANOMALY DETECTION IN CONTROLLER AREA NETWORK DATA USING HIERARCHICAL ANALYSIS.” 2018. Web. 07 Mar 2021.

Vancouver:

Flanagan B. ANOMALY DETECTION IN CONTROLLER AREA NETWORK DATA USING HIERARCHICAL ANALYSIS. [Internet] [Masters thesis]. Michigan Technological University; 2018. [cited 2021 Mar 07]. Available from: https://digitalcommons.mtu.edu/etdr/576.

Council of Science Editors:

Flanagan B. ANOMALY DETECTION IN CONTROLLER AREA NETWORK DATA USING HIERARCHICAL ANALYSIS. [Masters Thesis]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/576

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Open Access Theses and Dissertations