This work addresses whether a human-in-the-loop cyber-physical system (HCPS) can be effective in improving the longitudinal control of an individual vehicle in a traffic flow. We introduce the CAN Coach, which is a system that gives feedback to the human-in-the-loop using radar data (relative speed and position information to objects ahead) that is available on the controller area network (CAN). Using a cohort of six human subjects driving an instrumented vehicle, we compare the ability of the human-in-the-loop driver to achieve a constant time-gap control policy using only human-based visual perception to the car ahead, and by augmenting human perception with audible feedback from CAN sensor data. The addition of CAN-based feedback reduces the mean time-gap error by an average of 73%, and also improves the consistency of the human by reducing the standard deviation of the time-gap error by 53%. We remove human perception from the loop using a ghost mode in which the human-in-the-loop is coached to track a virtual vehicle on the road, rather than a physical one. The loss of visual perception of the vehicle ahead degrades the performance for most drivers, but by varying amounts. We show that human subjects can match the velocity of the lead vehicle ahead with and without CAN-based feedback, but velocity matching does not offer regulation of vehicle spacing. The viability of dynamic time-gap control is also demonstrated. We conclude that (1) it is possible to coach drivers to improve performance on driving tasks using CAN data, and (2) it is a true HCPS, since removing human perception from the control loop reduces performance at the given control objective.
翻译:这项工作解决了在轨人员网络物理系统(HCPS)能否有效改善交通流量中单个车辆的纵向控制。我们引入了CAN教练系统,这是一个使用控制区网络(CAN)提供的雷达数据(对前方物体的相对速度和位置信息)向行内人员提供反馈的系统。使用载着仪器车辆的六个人主体组群,我们比较了行内人员网络驱动器在使用基于人的视觉感知到前面的汽车实现固定的时间定位控制政策的能力,并通过从CAN传感器数据听到的反馈来提高人的视觉状态。基于CAN的反馈将平均时间定位错误减少73%,并通过将时间定位错误的标准偏差减少53%来提高人类的一致性。我们使用基于仪器的幽灵模式将人与行内行间驱动器的感知转换成虚拟车辆,而不是以有形的反馈方式来提高人与行能感知度。我们之所以能够对车辆的视觉性能进行精确控制,是因为在车辆的视觉性能上,我们可以对车辆的视觉性能进行预测,而能够对车辆进行精确控制。