Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning

Understand

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.