subject:(Intelligent Driver Model)

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1. Xiao, Weicheng. Safety evaluation of heterogeneous traffic: Experiments using different models in SUMO .

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

URL: http://hdl.handle.net/20.500.12380/302063

► With the development of self-driving technology, the day when autonomous vehicles share roads with traditional human-operated vehicles seems to be around the corner. This makes… (more)

▼ With the development of self-driving technology, the day when autonomous vehicles share roads with traditional human-operated vehicles seems to be around the corner. This makes the safety evaluation of this so-called heterogeneous traffic particularly important. In this thesis, by conducting microscopic heterogeneous traffic simulation on the simulation platform SUMO, the impact of autonomous vehicles on traffic safety and efficiency is studied. In order to obtain more accurate results, the initial car-following model and lane-changing model in SUMO need to be modified and calibrated using real-world data before being implemented in the simulation. As some previous research have shown, the types of vehicles involved on driving situation have an impact on drivers’ driving behaviours. The neglect of this impact has led to errors when reproducing the realistic driving behaviour with the existing car-following and lane-changing models. In this thesis, the models are modified by setting appropriate value for some related parameters to reflect this impact. Then the models are calibrated using the data extracted from highD dataset. Three performance indicators, namely number of conflicts, number of lane-changing and a speed performance indicator,are proposed to rate the error in terms of car-following, lane-changing and safety aspect. After the calibration, the best set of parameter values is selected and used to represent those human-operated vehicles in the heterogeneous traffic simulation. As for the autonomous vehicles, both zero-error Intelligent Driver Model (IDM) and Cooperative Adaptive Cruise Control (CACC) model are used to present two different types of autonomous vehicles. After getting all the models needed, the heterogeneous traffic simulation is conducted in SUMO. Several indicators, such as time to collision and number of lane-changing, etc., are used to evaluate the safety and efficiency of traffic. The results are different when using different autonomous car models. For the zero-error IDM case, the results show that traffic safety and efficiency increase as the penetration rate of autonomous vehicles increases. For the CACC model, the traffic efficiency increases with the increase in the penetration rate, but the traffic safety deteriorates when the penetration rate is low, and it slowly improves only after the penetration rate is higher than 0.5. The simulation results help to understand the impact that autonomous vehicles will bring on heterogeneous traffic.

Subjects/Keywords: heterogeneous traffic; microscopic simulation; intelligent driver model; highD dataset; traffic safety evaluation; mixed traffic

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APA (6^th Edition):

Xiao, W. (2020). Safety evaluation of heterogeneous traffic: Experiments using different models in SUMO . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/302063

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):

Xiao, Weicheng. “Safety evaluation of heterogeneous traffic: Experiments using different models in SUMO .” 2020. Thesis, Chalmers University of Technology. Accessed March 08, 2021. http://hdl.handle.net/20.500.12380/302063.

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

MLA Handbook (7^th Edition):

Xiao, Weicheng. “Safety evaluation of heterogeneous traffic: Experiments using different models in SUMO .” 2020. Web. 08 Mar 2021.

Vancouver:

Xiao W. Safety evaluation of heterogeneous traffic: Experiments using different models in SUMO . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/20.500.12380/302063.

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

Council of Science Editors:

Xiao W. Safety evaluation of heterogeneous traffic: Experiments using different models in SUMO . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/302063

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

Delft University of Technology

2. Blauw, Mathieu (author). Representing the Car-Following Behaviour of Adaptive Cruise Control (ACC) Systems Using Parametric Car-Following Models.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:c478714c-6449-4240-ab6d-154017e68dd8

► The Dutch governmental organisation Rijkswaterstaat contributes to the smooth and safe flow of traffic, as both traffic jams and accidents cost society large amounts of… (more)

▼ The Dutch governmental organisation Rijkswaterstaat contributes to the smooth and safe flow of traffic, as both traffic jams and accidents cost society large amounts of money each day. Roads are designed for the current traffic composition. Due to the promotion of Adaptive Cruise Control (ACC) systems, utilisation of these systems is expected to increase. Society benefits from insights into the effects these systems have on traffic flow, as they can help to reduce traffic jams and accidents. ACC systems are designed to increase driving comfort by taking over throttling and braking from the human driver. For optimal driver acceptance, these systems show similar driving behaviour to that of human drivers. However, this is not entirely possible due to limited anticipation. To predict how differences in driving behaviour affect traffic flows, researchers usually perform simulations using parametric car-following models. However, research shows contradictory findings. The goal of this research was to gain insights into the performance of commonly applied parametric car-following models on representing the driving behaviour of ACC systems. Optimal model calibration was obtained by investigating the sensitivity of the model calibration to synthetic data. Investigated were the calibration methodology and the quality and quantity of calibration data. Models are calibrated to real-world driving data from an Audi A4 from 2017. These models were used to assess the capability of representing typical highway scenarios: steady-state car-following, cut-in, cut-out, hard-braking and stop-and-go scenarios. The considered models were the Intelligent Driver Model (IDM) model, which has previously been applied to model the driving behaviour of human drivers, the newly developed simplified ACC (sACC) model and a variant on this model. Insights in the sensitivity of the model calibration were obtained by performing a sensitivity analysis on synthetic data. Essential factors in achieving an optimal model calibration are: 1) the model closely matches the driving behaviour in the data, 2) noise levels are as low as possible and 3) the data should contain as many situations as possible that are also included in the model. The dataset must be sufficiently long to include all these situations and to allow the model to develop its dynamics entirely. Using these insights, a calibration was performed on real-world ACC driving data from an Audi A4 (2017). For the ACC system, it was found: 1) the ACC system exhibits non-linear driving behaviour, 2) the acceleration depends on the current velocity and distance to the desired velocity, 3) the system does not consider an intelligent braking strategy and is thus not able of handling safety-critical driving situations and 4) the model includes a sub-controller which ensures comfortable driving behaviour. Except for the comfortable sub-controller, the non-linear IDM model considers all of these factors and thus best represents the driving behaviour. The linear sACC model cannot represent standing… Advisors/Committee Members: Happee, Riender (mentor), Knoop, Victor (mentor), Wang, Meng (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: ACC; Adaptive Cruise Control; Car-Following Models; Model Calibration; Calibration; Parametric; Rijkswaterstaat; IDM; Intelligent Driver Model

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

APA (6^th Edition):

Blauw, M. (. (2019). Representing the Car-Following Behaviour of Adaptive Cruise Control (ACC) Systems Using Parametric Car-Following Models. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c478714c-6449-4240-ab6d-154017e68dd8

Chicago Manual of Style (16^th Edition):

Blauw, Mathieu (author). “Representing the Car-Following Behaviour of Adaptive Cruise Control (ACC) Systems Using Parametric Car-Following Models.” 2019. Masters Thesis, Delft University of Technology. Accessed March 08, 2021. http://resolver.tudelft.nl/uuid:c478714c-6449-4240-ab6d-154017e68dd8.

MLA Handbook (7^th Edition):

Blauw, Mathieu (author). “Representing the Car-Following Behaviour of Adaptive Cruise Control (ACC) Systems Using Parametric Car-Following Models.” 2019. Web. 08 Mar 2021.

Vancouver:

Blauw M(. Representing the Car-Following Behaviour of Adaptive Cruise Control (ACC) Systems Using Parametric Car-Following Models. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 08]. Available from: http://resolver.tudelft.nl/uuid:c478714c-6449-4240-ab6d-154017e68dd8.

Council of Science Editors:

Blauw M(. Representing the Car-Following Behaviour of Adaptive Cruise Control (ACC) Systems Using Parametric Car-Following Models. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:c478714c-6449-4240-ab6d-154017e68dd8

University of Michigan

3. Rodriguez, Rodolfo. A Study on the Impact of Driver Behavior on the Energy Consumption of Electric Vehicles in a Virtual Traffic Environment.

Degree: MSin Engineering, Electrical Engineering, College of Engineering & Computer Science, 2020, University of Michigan

URL: http://hdl.handle.net/2027.42/154773

► Mild driving, i.e., accelerating slowly and smoothly, braking less frequently, and increasing spacing between vehicles to avoid harsh braking, has been shown to be effective… (more)

▼ Mild driving, i.e., accelerating slowly and smoothly, braking less frequently, and increasing spacing between vehicles to avoid harsh braking, has been shown to be effective in the improvement of fuel economy, especially for conventional vehicles with an internal combustion engine. For electric vehicles (EVs), it is implied that the energy consumption can also be improved by driving less aggressively. However, the extent in which the driver behavior reduces the energy consumption of an EV in various traffic environments has not been fully explored. A simulated environment can create a greater variety of driving cycles and conditions, thereby providing more insight as to how driving aggressiveness affects the vehicle’s energy consumption. The objective of this study is to evaluate the impact of the driving behavior on the energy consumption a battery electric vehicle (BEV) under various traffic scenarios. To simulate the driver behavior, a driver model is typically required to replicate the behavior of a human driver while traversing a given route (e.g., maintaining a safe distance from the preceding vehicle). Various driver models can be found in literature. Among these, the widely used Intelligent Driver Model is chosen in this study to characterize the different levels of driver aggressiveness. To that end, the microscopic traffic simulator, PTV Vissim, is used to simulate various realistic traffic environments in which a human driver’s behavior can be evaluated. The co-simulation of the PTV Vissim Component Object Model (COM) interface in conjunction with MATLAB allows the energy consumption performance on an EV to be determined for various levels of driving aggressiveness. The results obtained from the co-simulation with a virtual traffic environment are compared to those from single-lane car-following scenarios created using EPA (Environmental Protection Agency) standardized driving schedules. The results of the single-lane car-following scenario shows that there is a slight increase (<1.5%) in energy usage per kilometer by changing from a mild driving style to an aggressive driving style. For the city driving cycle created in Vissim, aggressive driving can lead to a 6.6% decrease in the average energy usage per kilometer driven than mild driving if it allows the vehicle to avoid red traffic signals and general vehicle traffic. However, driving at medium-level aggression is not quick enough to avoid these obstacles and consequently increases the average energy usage per kilometer by 1.1% over mild driving. For the highway driving cycle, the benefits of driving milder can be realized, as switching from aggressive to mild driving results in a 3.4% decrease in average energy usage per kilometer. The results of these driving tests demonstrate that the level of driving aggressiveness cannot be fixed and should instead adapt to the traffic environment in order to maximize the battery life and range of an EV. Advisors/Committee Members: Kim, Youngki (advisor), Mohammadi, Alireza (committee member), Hong, Junho (committee member).

Subjects/Keywords: Battery electric vehicle; Driver model; Aggressive driving; Energy consumption; Virtual traffic environment; PTV Vissim; Intelligent driver model (IDM); EPA driving schedule; Automotive engineering; Electrical engineering; Mechanical engineering

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

APA (6^th Edition):

Rodriguez, R. (2020). A Study on the Impact of Driver Behavior on the Energy Consumption of Electric Vehicles in a Virtual Traffic Environment. (Masters Thesis). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/154773

Chicago Manual of Style (16^th Edition):

Rodriguez, Rodolfo. “A Study on the Impact of Driver Behavior on the Energy Consumption of Electric Vehicles in a Virtual Traffic Environment.” 2020. Masters Thesis, University of Michigan. Accessed March 08, 2021. http://hdl.handle.net/2027.42/154773.

MLA Handbook (7^th Edition):

Rodriguez, Rodolfo. “A Study on the Impact of Driver Behavior on the Energy Consumption of Electric Vehicles in a Virtual Traffic Environment.” 2020. Web. 08 Mar 2021.

Vancouver:

Rodriguez R. A Study on the Impact of Driver Behavior on the Energy Consumption of Electric Vehicles in a Virtual Traffic Environment. [Internet] [Masters thesis]. University of Michigan; 2020. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/2027.42/154773.

Council of Science Editors:

Rodriguez R. A Study on the Impact of Driver Behavior on the Energy Consumption of Electric Vehicles in a Virtual Traffic Environment. [Masters Thesis]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/154773

KTH

4. Lindberg, Jonas. Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network.

Degree: Mathematical Statistics, 2020, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273629

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This thesis tackles the problem of predicting the collision risk for vehicles driving in complex traffic scenes for a few seconds into the future.… (more)

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This thesis tackles the problem of predicting the collision risk for vehicles driving in complex traffic scenes for a few seconds into the future. The method is based on previous research using dynamic Bayesian networks to represent the state of the system. Common risk prediction methods are often categorized into three different groups depending on their abstraction level. The most complex of these are interaction-aware models which take driver interactions into account. These models often suffer from high computational complexity which is a key limitation in practical use. The model studied in this work takes interactions between drivers into account by considering driver intentions and the traffic rules in the scene. The state of the traffic scene used in the model contains the physical state of vehicles, the intentions of drivers and the expected behaviour of drivers according to the traffic rules. To allow for real-time risk assessment, an approximate inference of the state given the noisy sensor measurements is done using sequential importance resampling. Two different measures of risk are studied. The first is based on driver intentions not matching the expected maneuver, which in turn could lead to a dangerous situation. The second measure is based on a trajectory prediction step and uses the two measures time to collision (TTC) and time to critical collision probability (TTCCP). The implemented model can be applied in complex traffic scenarios with numerous participants. In this work, we focus on intersection and roundabout scenarios. The model is tested on simulated and real data from these scenarios. %Simulations of these scenarios is used to test the model. In these qualitative tests, the model was able to correctly identify collisions a few seconds before they occur and is also able to avoid false positives by detecting the vehicles that will give way.

Detta arbete behandlar problemet att förutsäga kollisionsrisken för fordon som kör i komplexa trafikscenarier för några sekunder i framtiden. Metoden är baserad på tidigare forskning där dynamiska Bayesianska nätverk används för att representera systemets tillstånd. Vanliga riskprognosmetoder kategoriseras ofta i tre olika grupper beroende på deras abstraktionsnivå. De mest komplexa av dessa är interaktionsmedvetna modeller som tar hänsyn till förarnas interaktioner. Dessa modeller lider ofta av hög beräkningskomplexitet, vilket är en svår begränsning när det kommer till praktisk användning. Modellen som studeras i detta arbete tar hänsyn till interaktioner mellan förare genom att beakta förarnas avsikter och trafikreglerna i scenen. Tillståndet i trafikscenen som används i modellen innehåller fordonets fysiska tillstånd, förarnas avsikter och förarnas förväntade beteende enligt trafikreglerna. För att möjliggöra riskbedömning i realtid görs en approximativ inferens av tillståndet givet den brusiga sensordatan med hjälp av sekventiell vägd simulering. Två olika mått på risk studeras. Det första är baserat på…

Subjects/Keywords: Collision risk prediction; dynamic Bayesian network; sequential importance resampling; autonomous vehicles; ADAS; intelligent driver model; Förutsägelse av kollisionsrisk; dynamiskt Bayesianskt nätverk; sekventiell vägd simulering; autonoma fordon; ADAS; intelligent förarmodell; Probability Theory and Statistics; Sannolikhetsteori och statistik

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

APA (6^th Edition):

Lindberg, J. (2020). Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273629

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):

Lindberg, Jonas. “Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network.” 2020. Thesis, KTH. Accessed March 08, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273629.

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

MLA Handbook (7^th Edition):

Lindberg, Jonas. “Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network.” 2020. Web. 08 Mar 2021.

Vancouver:

Lindberg J. Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network. [Internet] [Thesis]. KTH; 2020. [cited 2021 Mar 08]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273629.

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

Council of Science Editors:

Lindberg J. Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network. [Thesis]. KTH; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273629

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

University of Queensland

5. Sharma, Anshuman. Understanding and Modelling the Car-Following Behaviour of Connected Vehicles.

Degree: School of Civil Engineering, 2019, University of Queensland

URL: http://espace.library.uq.edu.au/view/UQ:9981

Subjects/Keywords: Connected vehicles; Connected environment; Human factors; Driver compliance; Car-following; Intelligent driver model; Model calibration; Mixed traffic modelling; Driving simulator; model validation; 0905 Civil Engineering

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

APA (6^th Edition):

Sharma, A. (2019). Understanding and Modelling the Car-Following Behaviour of Connected Vehicles. (Thesis). University of Queensland. Retrieved from http://espace.library.uq.edu.au/view/UQ:9981

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):

Sharma, Anshuman. “Understanding and Modelling the Car-Following Behaviour of Connected Vehicles.” 2019. Thesis, University of Queensland. Accessed March 08, 2021. http://espace.library.uq.edu.au/view/UQ:9981.

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

MLA Handbook (7^th Edition):

Sharma, Anshuman. “Understanding and Modelling the Car-Following Behaviour of Connected Vehicles.” 2019. Web. 08 Mar 2021.

Vancouver:

Sharma A. Understanding and Modelling the Car-Following Behaviour of Connected Vehicles. [Internet] [Thesis]. University of Queensland; 2019. [cited 2021 Mar 08]. Available from: http://espace.library.uq.edu.au/view/UQ:9981.

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

Council of Science Editors:

Sharma A. Understanding and Modelling the Car-Following Behaviour of Connected Vehicles. [Thesis]. University of Queensland; 2019. Available from: http://espace.library.uq.edu.au/view/UQ:9981

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

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