Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2021-11-25
Abstract： Driver is the core of the "human-vehicle-environment" road traffic system. It is of great significance to study the perception-action pattern of drivers for standardizing driving behavior and improving safety level. However, there are few studies on head movement and organ cooperation during driving, especially on quantitative calculation. Therefore, this paper designed an experiment with the goal of uniform-speed driving in the virtual reality environment, and used information theory tools for modeling and analysis. We studied the cooperation of head, eye, hand and foot, proposed a causal network based on transfer entropy to describe the cooperation mode between the four, and proposed to use the network average transfer entropy as an indicator to evaluate the coordination of organs in driving. Finally, we found that head-eye cooperation is very strong in driving. The cooperation between organs is better when turning than when going straight.In the uniform-speed driving task, the priority of the driver to the action task is higher than the priority of the perception task."
Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2021-10-21
Abstract： Active information storage is an important growing popularity information theoretic tool, which has the advantage of being an easily accessible and interpretable store of information that is complex in the system. Driver head movement behaviour plays an important role in their directional control, yet this role has not been measured and explained quantitatively. In this paper, we apply active information storage to the study of driver head movements to investigate their relationship with driving directional smoothness. Specifically, we design a VR driving experiment with containing straight and turning directions, obtain a sequence of active driver head movement information storage and a sequence of vehicle deflection angles, and investigate the quantitative relationship between the two. We demonstrate a high temporal correlation between the two and use the joint entropy of the two as an indicator of driving performance. Finally, we use driver head movements to predict vehicle deflection angles in real time and obtain an accuracy of 88.56%. This work is expected to help monitor driver state and improve driving safety.
Peer Review Status:Awaiting Review
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