Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-veh...Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.展开更多
针对人机协同转向控制中对于驾驶员参与和驾驶员状态考虑较少这一问题,提出一种基于驾驶员状态预测的人机力矩协同(human-vehicle torque collaborative based on driver state prediction,HVTC-DSP)转向控制方法.该方法以力矩为人机交...针对人机协同转向控制中对于驾驶员参与和驾驶员状态考虑较少这一问题,提出一种基于驾驶员状态预测的人机力矩协同(human-vehicle torque collaborative based on driver state prediction,HVTC-DSP)转向控制方法.该方法以力矩为人机交互接口,提高了驾驶员的参与程度;同时,在控制器设计过程中采用模型预测控制方法,将驾驶员状态考虑在内,对驾驶员状态进行预测.采用高精度车辆仿真软件veDYNA进行仿真验证,结果表明,与不考虑驾驶员状态的人机协同力矩(human-vehicle torque collaborative based on no driver state prediction,HVTC-NDSP)转向控制方法相比,所提方法可以使辅助力矩更好地跟随驾驶员动作,提高车辆转向性能,减小侧向位移偏差,同时对不同驾驶员也有较好的适应性.进而,以驾驶员下一步动作为参考,使驾驶员当前力矩尽可能接近下一步期望的力矩,在转向性能几乎不受影响的情况下,适当减轻驾驶员操作负担.展开更多
文摘Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.
文摘针对人机协同转向控制中对于驾驶员参与和驾驶员状态考虑较少这一问题,提出一种基于驾驶员状态预测的人机力矩协同(human-vehicle torque collaborative based on driver state prediction,HVTC-DSP)转向控制方法.该方法以力矩为人机交互接口,提高了驾驶员的参与程度;同时,在控制器设计过程中采用模型预测控制方法,将驾驶员状态考虑在内,对驾驶员状态进行预测.采用高精度车辆仿真软件veDYNA进行仿真验证,结果表明,与不考虑驾驶员状态的人机协同力矩(human-vehicle torque collaborative based on no driver state prediction,HVTC-NDSP)转向控制方法相比,所提方法可以使辅助力矩更好地跟随驾驶员动作,提高车辆转向性能,减小侧向位移偏差,同时对不同驾驶员也有较好的适应性.进而,以驾驶员下一步动作为参考,使驾驶员当前力矩尽可能接近下一步期望的力矩,在转向性能几乎不受影响的情况下,适当减轻驾驶员操作负担.
