Driver-like decision-making method for vehicle longitudinal autonomous driving based on deep reinforcement learning

Decision-making is one of the key parts of the research on vehicle longitudinal autonomous driving. Considering the behavior of human drivers when designing autonomous driving decision-making strategies is a current research hotspot. In longitudinal autonomous driving decision-making strategies, traditional rule-based decision-making strategies are difficult to apply to complex scenarios. Current decision-making methods that use reinforcement learning and deep reinforcement learning construct reward functions designed with safety, comfort, and economy. Compared with human drivers, the obtained decision strategies still have big gaps. Focusing on the above problems, this paper uses the driver's behavior data to design the reward function of the deep reinforcement learning algorithm through BP neural network fitting, and uses the deep reinforcement learning DQN algorithm and the DDPG algorithm to establish two driver-like longitudinal autonomous driving decision-making models. The simulation experiment compares the decision-making effect of the two models with the driver curve. The results shows that the two algorithms can realize driver-like decision-making, and the consistency of the DDPG algorithm and human driver behavior is higher than that of the DQN algorithm, the effect of the DDPG algorithm is better than the DQN algorithm.