School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China.
School of Medicine, Tsinghua University, Beijing, 100084, China.
Sci Rep. 2023 Sep 22;13(1):15839. doi: 10.1038/s41598-023-41549-9.
For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain-computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers' mental activities in low-risk episode and high-risk episode were compared, the influences on passengers' mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers' driving risk cognition.
对于高级别的自动驾驶车辆，人类作为乘客而不是驾驶员，无需操作车辆，这使得高级别的自动驾驶车辆的脑机接口系统依赖于乘客的大脑状态，而不是驾驶员的大脑状态。特别是在面临具有挑战性的驾驶情况时，如何将乘客的精神状态转化为安全驾驶是未来的重要选择。量化乘客的驾驶风险认知是实现这一目标的基本步骤。在本文中，比较了低风险和高风险阶段乘客的心理活动，首先通过 fNIRS 探索了驾驶场景风险对乘客心理活动的影响。结果表明，驾驶场景风险引起的乘客在布罗德曼区 10 的心理活动非常活跃，这通过检查相应挑战性实验中收集的真实驾驶数据得到了验证，并且大脑中的氧气与驾驶风险场之间存在正相关关系。这一初步发现为设计以人为中心的智能系统提供了一种可能的解决方案，该系统可以利用乘客的驾驶风险认知，为高级别的自动驾驶车辆提供安全的驾驶。
