A Vehicle Adaptive Cruise Control Method Based on Deep Reinforcement Learning Algorithm

Adaptive cruise control (ACC) is an upgrade to the traditional cruise control system in vehicles. This paper presents an adaptive cruise control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The Actor network computes actions based on the current state, adding noise to enhance exploration. The Critic network computes the Q-value of the current state-action pair. The Actor target network and Critic target network compute the Q-value of the next state-action pair. The gradient descent method is used to minimize the loss function of the Critic network, which includes the error between the real Q-value and the target Q-value. By integrating an Actor-Critic network, the system demonstrates improved adaptability and efficiency over traditional ACC methods, as shown through simulated experiments