Xia Yingji, Geng Maosi, Chen Yong, Sun Sudan, Liao Chenlei, Zhu Zheng, Li Zhihui, Ochieng Washington Yotto, Angeloudis Panagiotis, Elhajj Mireille, Zhang Lei, Zeng Zhenyu, Zhang Bing, Gao Ziyou, Chen Xiqun Michael
Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Polytechnic Institute & Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310015, China.
Patterns (N Y). 2023 Apr 18;4(7):100730. doi: 10.1016/j.patter.2023.100730. eCollection 2023 Jul 14.
Autonomous vehicles will share roads with human-driven vehicles until the transition to fully autonomous transport systems is complete. The critical challenge of improving mutual understanding between both vehicle types cannot be addressed only by feeding extensive driving data into data-driven models but by enabling autonomous vehicles to understand and apply common driving behaviors analogous to human drivers. Therefore, we designed and conducted two electroencephalography experiments for comparing the cerebral activities of human linguistics and driving understanding. The results showed that driving activates hierarchical neural functions in the auditory cortex, which is analogous to abstraction in linguistic understanding. Subsequently, we proposed a neural-informed, semantics-driven framework to understand common human driving behavior in a brain-inspired manner. This study highlights the pathway of fusing neuroscience into complex human behavior understanding tasks and provides a computational neural model to understand human driving behaviors, which will enable autonomous vehicles to perceive and think like human drivers.
在向完全自主运输系统的过渡完成之前，自动驾驶车辆将与人类驾驶的车辆共用道路。提高两种车辆类型之间相互理解的关键挑战，不能仅通过将大量驾驶数据输入数据驱动模型来解决，还需要使自动驾驶车辆能够理解并应用类似于人类驾驶员的常见驾驶行为。因此，我们设计并进行了两项脑电图实验，以比较人类语言和驾驶理解的大脑活动。结果表明，驾驶激活了听觉皮层中的分层神经功能，这类似于语言理解中的抽象。随后，我们提出了一个神经启发、语义驱动的框架，以大脑启发的方式理解常见的人类驾驶行为。这项研究突出了将神经科学融入复杂人类行为理解任务的途径，并提供了一个计算神经模型来理解人类驾驶行为，这将使自动驾驶车辆能够像人类驾驶员一样感知和思考。
