基于认知-控制框架的侧风工况下驾驶员横向控制模型研究

Research on Driver Lateral Control Model Under Crosswind Conditions Based on Cognitive-Control Framework

本文中基于神经工效学认知理论融入驾驶员预瞄模型,建立了以认知-控制为框架的驾驶员横向控制模型。模型采用Simulink和TruckSim软件联合仿真的形式验证。采用粒子群优化算法对控制框架中的比例-微分(PD)控制器模块参数进行优化标定。结果表明,在侧风工况下,基于认知-控制为框架所建立的驾驶员横向控制模型有效(RMSE=0.09),且精度更高,适应度更广。另外,从认知-控制角度改变预览时间tp、增益比例kp和微分参数kd,可表征不同驾驶风格的驾驶员行为。本研究为提高侧风工况下的高级辅助驾驶系统和自动驾驶汽车的安全性和舒适性提供参考思路。

In this paper,a driver lateral control model with cognitive-control as framework is proposedbased on neuro-ergonomic cognitive theory and driver preview model. The lateral control model is verified by the cosimulation of Simulink and Truck Sim software. The particle swarm optimization algorithm is used to optimize andcalibrate the parameters of the proportional-derivative controller module in control framework. The results show thatin crosswind condition,the driver lateral control model built with cognitive-control framework is effective(RMSE=0.09),with higher accuracy and wider adaptability. In addition,from the perspective of cognitive-control,by chang-ing the preview time tp,gain ratio kpand differential parameter kd,the behaviors of drivers with different driving stylecan be characterized. The research provides a reference thinking for enhancing the safety and comfort of advanceddriving assisted systems and autonomous vehicles under crosswind conditions.