This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Game Theory and Reinforcement Learning based Smart Lane Change Strategies
Technical Paper
2022-01-0221
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
With the development of science and technology, breakthroughs have been made in the fields of intelligent algorithms, environmental perception, chip embedding, scene analysis, and multi-information fusion, which together prompted the wide attention of society, manufacturers and owners of autonomous vehicles. As one of the key issues in the research of autonomous vehicles, the research of vehicle lane change algorithm is of great significance to the safety of vehicle driving. This paper focuses on the conflict of interest between the lane-changing vehicle and the target lane vehicle in the fully autonomous driving environment, and proposes the method of coupling kinematics and game theory and reinforcement learning based optimization, so that when the vehicle is in the process of lane changing game, the lane-changing vehicle and the target lane vehicle can make decisions that are beneficial to the balance of interests of both sides. Firstly the conditions for judging whether the lane-changing vehicle and the target lane vehicle are in the lane-changing game are provided. According to the actual vehicle driving situation, the type of payoff in the game between the two vehicles is then determined. The payoff function is designed by using the kinematic method, and the corresponding total payoff value under the different strategy combinations of the two vehicles is obtained. Then, the game payoff matrix is analyzed, and the optimal strategy combination and the corresponding acceleration are obtained. Finally, the optimal strategy combination of the two vehicles is determined, which can effectively avoid the safety problem, and the overall payoff of the two sides is relatively balanced. The results of experience indicate that two vehicles will choose the most beneficial strategy combination and then make their own decisions according to the proposed model, at the same time, this paper also respectively analyzes the payoffs of the vehicles in the velocity and the relative position of different cases to verify the feasibility and rationality of the model.
Authors
Topic
Citation
Zhan, Z., LU, G., wang, K., LI, J. et al., "Game Theory and Reinforcement Learning based Smart Lane Change Strategies," SAE Technical Paper 2022-01-0221, 2022, https://doi.org/10.4271/2022-01-0221.Also In
References
- Hartenstein , H.K.P. VANET Vehicle Network Technology and Application Tsinghua University Press 2013
- Yuke , L. and Liu , Y. Development Status and Suggestions of Domestic Intelligent Connected Vehicles Auto and Parts 41 2016 56 59
- Althoff , M. , Stursberg , O. , and Buss , M. Model-Based Probabilistic Collision Detection in Autonomous Driving IEEE Transactions on Intelligent Transportation Systems 10 2 2009 299 310
- Li , J. , Zhao , X. , Cho , M.J. et al. From Trolley to Autonomous Vehicle: Perceptions of Responsibility and Moral Norms in Traffic Accidents with Self-Driving Cars Bulletin of the Korean Chemical Society 31 6 2016 1782 1784
- Rudin Brown , C.M. , Young , K.L. , Patten , C. , Lenné , M.G. et al. Driver Distraction in an Unusual Environment; Effect of Text-Messaging in Tunnels Rev. Bras. Ginecol. Obstet 29 4 2013 331 334
- Ruifang , Z. Research on the Competitive View of Contemporary College Students Huazhong University of Science and Technology 2005
- Gipps , P.G. A Model for the Structure of Lane-Changing Decisions Transportation Research Part B Methodological 20 5 1986 403 414
- Ahmed , K.I. and Ahmed , K.I. Modeling Drivers' Acceleration and Lane Changing Behavior Massachusetts Institute of Technology 2005
- Deyi , Li 2016/11/23 http://www.tuicool.com/articles/Ubq6fmR
- Duffield , T.J. and Krupenia , S. Drivers’ Interaction Preferences in Autonomous Vehicle Multimodal Interactive Systems[C]//Human Factors and Ergonomics Society Meeting SAGE Publications 2015 1302 1306
- Xiaoming , L. , Zheng , S. , and Xinchun , J. Vehicle Lane-Changing Model Based on Dynamic Repeated Game Highway Traffic Technology 25 006 2008 120 125
- Jin-Shuan , P. , Rui , F. , Lei-Lei , S. , and Qiong , Z. Research of Driver’s Lane Change Decision-Making Mechanism Journal of Wuhan University of Technology 33 12 2011 46 50
- Kim , C. and Langari , R. Game Theory Based Autonomous Vehicles Operation International Journal of Vehicle Design 65 4 2014 360 383
- Elhenawy , M. , Elbery , A.A. , Hassan , A.A. , Rakha , H.A. An Intersection Game-Theory-Based Traffic Control Algorithm in a Connected Vehicle Environment 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015) IEEE 2015
- Fang , W. , Xiaoyuan , W. , Zhenxue , L. et al. Lane Selection Model Based on Complete Information Multiplayer Dynamic game Computer Engineering and Application 054 023 2018 238 245
- Kita , H. A Merging-Giveway Interaction Model of Cars in a Merging Section: A Game Theoretic Analysis Transportation Research Part A Policy & Practice 33 3-4 1999 305 312
- Wang , M. , Hoogendoorn , S.P. , Daamen , W. , van Arema , Bart , Happeeb , Riender Game Theoretic Approach for Predictive Lane-Changing and Car-Following Control Transportation Research Part C Emerging Technologies 2015 58 Part A 73 92
- Cortés-Berrueco , L.E. , Gershenson , C. , and Stephens , C.R. Traffic Games: Modeling Freeway Traffic with Game Theory Plos One 11 11 2016 e0165381
- Talebpour , A. , Mahmassani , H.S. , and Hamdar , S.H. Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach Transportation Research Part C Emerging Technologies 59 2015 216 232
- Weiying , Z. Game Theory and Information Economics Gezhi Publishing House 2012
- Dempster , A.P. 2008 57 72
- Shakarian , P. , Roos , P. , and Johnson , A. A Review of Evolutionary Graph Theory with Applications to Game Theory Biosystems 107 2 2012 66 80
- Treiber , M. and Helbing , D. 2002