Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.

中文翻译：

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人机协作驾驶的驾驶能力下降检测。

由于当前技术和产品成本的限制，尤其是对于公共交通而言，人仍处于驱动循环中。协同驾驶的一个关键问题是确定驾驶员需要协助的时间。为了克服基于驾驶员状态的检测算法的缺点，提出了一种新的指标，称为驾驶员的校正能力，并与驾驶风险进一步结合，以评估驾驶能力。基于此测量，将进一步设置降级域（DD），以检测驱动能力的降级。对数正态分布用于根据台架测试数据对DD的边界进行建模，并设计了一种在线算法以交互方式更新其参数以识别各个驾驶方式。台架验证结果表明，DD边界的识别算法收敛良好，可以反映各个驾驶特性。所提出的退化检测算法可以用于确定从手动驾驶到自动驾驶的切换时间，并且这种基于DD的协同驾驶系统可以在安全条件下驾驶车辆。