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A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

Résumé

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.
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Dates et versions

hal-01980542 , version 1 (14-01-2019)

Identifiants

  • HAL Id : hal-01980542 , version 1

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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. ⟨hal-01980542⟩
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