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 closedform 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.
In one previous work REF , a reinforcement learning based approach was presented for automated lane change maneuver, which allows the agent to explore the unforeseen environment and make the correct decision based on the Q-values.
A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers
2018 IEEE Intelligent Vehicles Symposium (IV)
2018 IEEE Intelligent Vehicles Symposium (IV)
Computer Science Engineering