The decision-making layer of vehicle longitudinal autonomous driving decides the ideal action instruction according to the current motion state of the vehicle and environmental information. At present， how to consider the behavior of human drivers in autonomous driving decision-making strategies has become a hotspot. In longitudinal autonomous driving decision-making strategies， traditional rule-based decision-making strategies are difficult to be applied to complex scenarios. Current decision-making methods use reinforcement learning and deep reinforcement learning to construct reward functions designed with safety， comfort， and economy formulas. The obtained decision-making strategy still has a big gap compared with that of the human driver. To solve the above problems， this paper uses driver data to design a reward function by BP neural network， and uses DDPG algorithm to establish a driver-like longitudinal autonomous driving decision-making method. Finally， the effectiveness of the method and the consistency with the driver's behavior are verified by simulation tests.
