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arXiv · 1804.07871

A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

Pin Wang·Ching-Yao Chan·Arnaud de La Fortelle
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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 proposed 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 treated both state space and action space as continuous, and designed a Q-function approximator that has a closed- form greedy policy, which contributes to the computation efficiency of our deep Q-learning algorithm. Extensive simulations are conducted for training the algorithm, 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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Pin Wang, Ching-Yao Chan, Arnaud de La Fortelle. 2018-04-21. A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers. https://arxiv.org/abs/1804.07871

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