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. In this project, we use a Double Deep Q-Network, along with rule-based constraints to make lane-changing decision. A safe and efficient lane change behavior may be obtained by combining high-level lateral decision-making with low-level rule based trajectory monitoring.
A Non-stationary environment is an environment where sudden concept drift can occur due to dynamic and unknown probability data distribution function. In our case, the highway is perfectly a non-stationary envi- ronment since many of the state variables can be changed randomly without the interference of the considered agent.
- python
- tensorflow
- sumolib
- traci