Lane Change Decision-Making through Deep Reinforcement Learning
This addresses the problem of safe and efficient lane changes for autonomous vehicles, but it is incremental as it builds on existing DQN methods with added rules.
The paper tackled lane change decision-making in autonomous driving by using a Deep Q-Network with rule-based constraints, resulting in a rule-based DQN that achieved a safety rate of 0.8 and average speed of 47 MPH, outperforming a standard DQN method.
Due to the complexity and volatility of the traffic environment, decision-making in autonomous driving is a significantly hard problem. In this project, we use a 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. The agent is anticipated to perform appropriate lane-change maneuvers in a real-world-like udacity simulator after training it for a total of 100 episodes. The results shows that the rule-based DQN performs better than the DQN method. The rule-based DQN achieves a safety rate of 0.8 and average speed of 47 MPH