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.
A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers
2018 IEEE Intelligent Vehicles Symposium (IV) ; 1379-1384
2018-06-01
1368645 byte
Conference paper
Electronic Resource
English
A REINFORCEMENT LEARNING BASED APPROACH FOR AUTOMATED LANE CHANGE MANEUVERS
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