We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning frameworks with quadratic approximator for deciding how to select a comfortable gap and just follow the preceding vehicle. Finally, a polynomial lane change trajectory is generated and Pure Pursuit Control is implemented for path tracking. We demonstrate the effectiveness of this framework in simulation, from both the decision-making and control layers. The proposed architecture also has the potential to be extended to other autonomous driving scenarios.
@article{arxiv.1904.10171,
title = {Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning},
author = {Tianyu Shi and Pin Wang and Xuxin Cheng and Ching-Yao Chan and Ding Huang},
journal= {arXiv preprint arXiv:1904.10171},
year = {2019}
}