In order to improve the adaptability and tracking performance of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the real driver in the driver–vehicle–road closed-loop system, a kind of adaptive preview time model for intelligent vehicle driver model is proposed. This article builds the intelligent vehicle driver model based on optimal preview control theory and the basic preview time is identified to minimize path error under various conditions based on particle swarm optimization. Then, the ideal compensation preview time is constructed in various conditions and the appropriate factors affecting compensation preview time are filtered out according to correlation analysis. Moreover, the architecture and training procedure of deep network is specified for compensation preview time prediction. Finally, the adaptive preview time is modeled by combining the basic preview time with the compensation preview time and the validity of adaptive preview time model is verified by the driver–vehicle–road closed-loop system under normal and aggressive driving conditions. The results show that the proposed adaptive preview time model can help intelligent vehicles better adapt complex driving conditions and effectively improve the path-following performance.

为提高智能汽车在复杂驾驶条件下的适应性和跟踪性能，模拟真实驾驶员在人-车-路闭环系统中的操纵特性，提出了一种智能汽车驾驶员模型自适应预览时间模型被提议。本文基于最优预览控制理论建立了智能车辆驾驶员模型，并基于粒子群优化确定了基本预览时间，以最小化各种条件下的路径误差。然后在各种条件下构建理想的补偿预览时间，并根据相关性分析筛选出影响补偿预览时间的适当因素。此外，为补偿预览时间预测指定了深度网络的架构和训练程序。最后，结合基本预览时间和补偿预览时间对自适应预览时间进行建模，并通过正常和激进驾驶条件下的驾驶员-车辆-道路闭环系统验证自适应预览时间模型的有效性。结果表明，所提出的自适应预览时间模型可以帮助智能车辆更好地适应复杂的驾驶条件，有效提高路径跟随性能。