跟车工况下基于风险评估的人机共驾策略

Human Machine Co-driving Strategy Based on Risk Assessment Under Car Following Conditions

为了避免驾驶辅助系统对驾驶员造成不必要的干预,结合碰撞风险与驾驶操纵能力引出了纵向跟车工况下的风险评估区的概念,基于驾驶数据的正态分布特性确定了风险评估区边界,进而提出一种新的人机共驾纵向驾驶权分配策略,该策略以碰撞时间倒数(TTCi)为判断前提,如果TTCi超过其阈值,则以风险评估区上边界代表驾驶操纵能力的最大偏差值,根据驾驶员操纵能力的偏差程度分配辅助驾驶系统的控制权。结合Prescan、Matlab/Simulink与罗技G29驾驶模拟器搭建了驾驶员在环仿真平台,以分心驾驶模拟驾驶员操纵能力下降情况,对策略的有效性进行了验证。结果表明,在高速道路跟车工况下,所提出的人机共驾策略能有效避免由于驾驶员操纵能力下降导致的碰撞事件发生。

To avoid unnecessary interventions by the driver assistance system,this paper combines collision risk and driving maneuverability to introduce the concept of a risk assessment zone in longitudinal following scenarios.The boundary of this zone is determined based on the normal distribution characteristics of the driving data.Subsequently,a new human-machine co-driving longitudinal driving rights allocation strategy is proposed,which takes the inverse time to collision(TTCi)as the basis for judgment.If the TTCi exceeds the threshold value,the upper boundary of the risk assessment zone represents the maximum deviation in driving maneuverability.The control rights of the assistance system are allocated according to the deviation in the driver's maneuverability.By combining Prescan,Matlab/Simulink and the Logitech G29 driving simulator,a driver-in-the-loop simulation platform was constructed.The platform simulated the reduced driver maneuverability due to distracted driving,thereby verifying the effectiveness of the strategy.The results show that the proposed human-machine co-driving strategy can effectively prevent collisions caused by reduced driver maneuverability under high-speed road following conditions.