Human Machine Co-driving Strategy Based on Risk Assessment Under Car Following Conditions
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.
collision riskdriver's manoeuvring abilityrisk assessment areanormal distributiondistribution of driving rights
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