Deep Reinforcement Learning Teaches Autonomous Cars to Change Lanes Safely and Smoothly

Without the attention mechanism, obstacle avoidance success rate dropped by 2.71 percent.

The risk field contributed most significantly to safety—its removal caused a 13.25 percent drop in obstacle avoidance success rate—while the improved replay mechanism accelerated convergence, with its absence resulting in a 15.69 percent decrease in obstacle avoidance success rate.

Beyond raw success rates, the method demonstrated superior trajectory quality in overtaking and obstacle avoidance scenarios.

Subject of Research: Multi-objective optimization of lane-changing trajectories for autonomous vehicles based on deep reinforcement learning Article Title: Multi-objective optimization of lane-changing trajectories for autonomous vehicles based on deep reinforcement learning Article References: Hu, Y., & Li, Q.

Multi-objective optimization of lane-changing trajectories for autonomous vehicles based on deep reinforcement learning.