Dual-Layer Path Planning Model for Autonomous Vehicles in Urban Road Networks Using an Improved Deep Q-Network Algorithm with Proportional–Integral–Derivative Control

With the continuous progress of intelligent transportation systems and automated driving technologies, complex urban road environments put forward higher requirements on the real-time characteristic and accuracy of path planning algorithms. Traditional single-layer path planning methods struggle to effectively handle the complexity of road and lane networks, leading to high computational complexity and suboptimal planning outcomes. To address this issue, we propose a dual-layer path planning model. First, at the road level, we employ the A* algorithm to efficiently determine the optimal macroscopic route, reducing computational overhead. At the lane level, we introduce a Proportional–Integral–Derivative Q-network (PIDQN) based on deep reinforcement learning, which leverages PID control mechanisms to enhance lane selection accuracy and adaptability. By incorporating proportional, integral, and derivative control, PIDQN effectively handles dynamic environments and avoids local optima, ensuring stable and faster convergence. Compared with traditional Deep Q-Network (DQN) and Q-learning algorithms, PIDQN demonstrates significant improvements in success rate and convergence speed in path planning tasks. Using high-precision maps in real-world environments and Python for simulation experiments, we verify the superiority of this approach in complex urban road networks, and we compare the performance of traditional A* algorithms and two-layer planning algorithms. The results show that the two-layer planning algorithm outperforms the traditional A* algorithm and provides a more robust and efficient solution for self-driving car navigation.