针对当前驾驶员模型无法体现驾驶操纵熟练程度的缺点,利用遗传算法的自动寻优能力,总结驾驶员自学习与驾驶经验特点,遵循行驶误差最小与体力负担最小原则,对模糊PID比例因子和量化因子进行离线优化设计,以此模拟驾驶员从生手到熟练驾驶培训过程。构建包括遗传算法优化的方向模糊PID与速度模糊综合控制驾驶员模型以及整车行驶动力学模型在内的人车闭环系统仿真模型,在纵向速度单向变化、侧向双移线工况与大曲率试验道路典型工况下进行仿真分析。结果表明：基于遗传算法优化的方向模糊PID与速度模糊综合控制模型可以很好地描述驾驶员在纵向加减速操纵特性以及侧向预期轨迹跟随转向驾驶特性,相比于传统PID与模糊PID控制,具有更好的纵向加减速操纵特性与侧向预期轨迹跟随性能。

To overcome the shortcoming that driving manipulation qualification can’t be embodied by current driver models, the automatic optimization ability of genetic algorithms is adopted to summarize drivers’ self-learning features and driving experience, off-line optimize fuzzy PID scale factor and quantization factor by following running error minimum and physical ability-to-pay minimum principle, and simulate the driving training processes from a green hand to a skilled driver. Then a driver-vehicle closed loop system simulation model including direction fuzzy PID optimized by genetic algorithms, speed fuzzy integrated control driver model and entire vehicle riding dynamics model is established, which simulates and analyzes typical modes, such as longitudinal speed one-way variation, lateral double lane and big curvature test road. The simulation results show that the model can well describe drivers’ longitudinal acceleration/deceleration manipulating characteristics and lateral desired track following steering riding characteristics. And when it’s compared with traditional PID and fuzzy PID, it has better longitudinal acceleration/deceleration manipulating characteristics and lateral desired track following steering riding characteristics.

国家自然科学基金资助项目(50475066);重庆市科委攻关项目(2010AC6049)