Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach
The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advan...
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| Published in | IEEE transactions on intelligent transportation systems Vol. 21; no. 1; pp. 433 - 443 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications. |
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| AbstractList | The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications. |
| Author | Zhou, Mofan Yu, Yang Qu, Xiaobo |
| Author_xml | – sequence: 1 givenname: Mofan surname: Zhou fullname: Zhou, Mofan organization: Tencent Holdings Limited, Shenzhen, China – sequence: 2 givenname: Yang orcidid: 0000-0001-6751-5293 surname: Yu fullname: Yu, Yang organization: School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, Australia – sequence: 3 givenname: Xiaobo orcidid: 0000-0003-0973-3756 surname: Qu fullname: Qu, Xiaobo email: drxiaoboqu@gmail.com organization: Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden |
| BackLink | https://research.chalmers.se/publication/515046$$DView record from Swedish Publication Index |
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| Snippet | The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed... |
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| StartPage | 433 |
| SubjectTerms | Automation Car following deep deterministic policy gradient Driver behavior Driving intersection Intersections Learning machine learning Neural network Optimization Oscillators Real-time systems Reinforcement learning Traffic information traffic light traffic oscillation Traffic signals Training Trajectory Vehicles |
| Title | Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach |
| URI | https://ieeexplore.ieee.org/document/8848852 https://www.proquest.com/docview/2333537821 https://research.chalmers.se/publication/515046 |
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