Huang, Ding

1paper

1 Paper

17.6ROApr 23, 2019
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning

Tianyu Shi, Pin Wang, Xuxin Cheng et al.

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.