A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

Pin Wang 1 Ching-Yao Chan 1 Arnaud De La Fortelle 2
1 University of California [Berkeley]
2 CAOR - Centre de Robotique
Abstract : Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we propose a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treat both state space and action space as continuous, and design a unique format of Q-function approximator to estimate the total return which is an accumulated reward over a lane changing process. Extensive simulations are conducted for training the algorithms, and the results illustrate that the Reinforcement Learning based vehicle agent is capable of learning a smooth and efficient driving policy for lane change maneuvers.
Keywords : Reinforcement Learning Autonomous Driving Lane Change Vehicle Control
Type de document :
Communication dans un congrès
IEEE Intelligent Vehicles Symposium, Jun 2018, Chang Shu, China. IEEE, 2018 IEEE Intelligent Vehicles Symposium (IV)
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01980542
Contributeur : Arnaud De La Fortelle <>
Soumis le : lundi 14 janvier 2019 - 15:00:06
Dernière modification le : jeudi 7 février 2019 - 15:30:29

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Pin Wang, Ching-Yao Chan, Arnaud De La Fortelle. A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers. IEEE Intelligent Vehicles Symposium, Jun 2018, Chang Shu, China. IEEE, 2018 IEEE Intelligent Vehicles Symposium (IV). 〈hal-01980542〉

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