The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

驾驶员模型是智能汽车的决策与控制中心。为了提高智能车辆在复杂驾驶条件下的适应性，并模拟熟练驾驶员在驾驶员-车辆-道路闭环系统下的操纵特性，提出了一种基于人为增强的智能汽车类纵向驾驶员模型。建议学习。本文基于最优预见控制理论建立了智能车辆的横向驾驶员模型。然后，建立纵向驾驶员模型的控制校正链接，以计算所需纵向加速度的节气门开度或制动踏板行程。此外，用于纵向驾驶员模型的强化学习代理通过综合评估指标和熟练的驾驶员数据进行并行训练。最后，在模拟实验和真实汽车测试之间进行训练性能和场景验证，以验证基于强化学习的纵向驾驶员模型的有效性。结果表明，所提出的基于强化学习的类人纵向驾驶员模型可以帮助智能车辆有效地模仿熟练驾驶员在各种路径跟踪情况下的速度控制行为。