Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints

Understand

Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment.

• Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study.
• Through the combination of high-level lateral decision-making and low-level rule-based trajectory modification, a safe and efficient lane change behavior can be achieved.
• With the setting of our state representation and reward function, the trained agent is able to take appropriate actions in a real-world-like simulator.