一种新的考虑驾驶员疲劳的人机协同避障策略

A New Human-machine Cooperative Obstacle Avoidance Strategy Considering Driver Fatigue

人机协同驾驶系统在避障工况中,针对驾驶员注意力不集中导致不能及时操控汽车的问题,设计一种考虑驾驶员特性的人机协同避障控制器.以双驾双控人机共驾结构为基础,自动驾驶系统采用线性二次调节器(Linear Quadratic Regulator,LQR)控制车辆跟踪规划轨迹,基于最优预瞄侧向加速度建立驾驶员模型.为了使驾驶权在避障过程中分配更加合理,使用驾驶模拟器采集疲劳驾驶数据训练BP神经网络来识别驾驶员疲劳操作行为,通过对空间碰撞危险度和驾驶员疲劳因子建模设计人机协同避障策略.搭建Carsim、PreScan和Simulink联合仿真平台进行高速工况下的避障仿真试验.仿真结果表明,相较于传统方法,本文所提策略在静态障碍物避障过程中,侧向加速度、质心侧偏角和横摆角速度分别降低了8.9%、18.2%和11.1%,在动态障碍物避障过程中,相应的指标分别降低了51.5%、53.4%和50.6%,提高了车辆在避障过程中的稳定性.

In the obstacle avoidance condition of the human-machine cooperative driving system,a human-machine cooperative obstacle avoidance controller is designed considering the driver’s characteristics,aiming at the problem that the driver cannot control the car in time due to the driver’s inattention.Based on the dual-driver and dual-control man-machine co-driving structure,the automatic driving system uses a Linear Quadratic Regulator(LQR)to control the vehicle to track the planned trajectory,and the driver model is established based on the optimal preview lateral acceleration model.To allocate driving rights more reasonably in the process of obstacle avoidance,a driving simulator is used to collect fatigue driving data to train a BP neural network to identify the driver’s fatigue operation behavior.The human-machine cooperative obstacle avoidance strategy is designed by modeling the driver fatigue factor and space risk of collision.Carsim,PreScan and Simulink co-simulation platform is built to conduct obstacle avoidance simulation experiments under high-speed conditions.The simulation results show that,compared with the traditional method,the proposed strategy reduces the lateral acceleration,sideslip angle and yaw rate by 8.9%,18.2%and 11.1%,respectively,during the static obstacle avoidance process,and in the process of dynamic obstacle avoidance,the corresponding indicators are reduced by 51.5%,53.4%and 50.6%,respectively.Therefore,the stability of the vehicle during obstacle avoidance is improved.