Vulnerable Road Users (VRUs) present a significant challenge for road safety due to the frequent unpredictability of their behaviors. In typical Intelligent Transportation Systems, vision-based approaches supported by networked cameras are often used to anticipate VRUs motion intentions and trajectories. However, several limitations posed by occlusions and distractions set a boundary for the efficacy of such methods. To address these challenges, this study introduces a framework that leverages data collected using wearable neurophysiological sensors on VRUs to integrate them seamlessly into the Vehicle-to-Everything communication framework. This integration empowers VRUs to autonomously broadcast their intended movements to other road agents, especially autonomous vehicles, thereby bridging a critical gap in current vehicular communication systems. To validate this concept, we conducted an experiment involving 12 participants, from whom EEG signals were collected as they engaged in road-crossing decisions within simulated environments. Employing Hidden Markov Models, we identified four cognitive stages intrinsic to a pedestrian's decision-making process. Our statistical analysis further revealed significant variations in EEG activities across these stages, shedding light on the neural correlates and cognitive dynamics underpinning pedestrian road-crossing behavior. We then developed a predictive cognitive model using dynamic time warping and K-nearest neighbors algorithms, optimized through a data-driven sliding window approach. This model demonstrated high predictive accuracy, evidenced by an Area Under the Curve of 0.91, indicating its capability to anticipate pedestrian road-crossing actions approximately 1 second in advance of any pedestrian movement. This research paves the way for a novel VRU-Vehicle interaction paradigm and signifies a shift towards a forward-thinking ecosystem.

翻译：弱势道路使用者（VRUs）因其行为频繁不可预测，对道路安全构成重大挑战。在典型的智能交通系统中，通常采用基于视觉的方法，并依托网络摄像头来预测VRUs的运动意图与轨迹。然而，遮挡与干扰带来的若干局限为此类方法的有效性设置了边界。为应对这些挑战，本研究提出一种框架，利用可穿戴神经生理传感器在VRUs上采集的数据，将其无缝整合至车联网通信框架中。该整合使VRUs能够自主地向其他道路参与者（尤其是自动驾驶车辆）广播其预期移动意图，从而弥补了当前车辆通信系统中的一个关键缺口。为验证此概念，我们开展了一项实验，招募12名参与者在模拟环境中进行道路穿越决策，并同步采集其脑电图信号。通过隐马尔可夫模型，我们识别出行人决策过程中固有的四个认知阶段。统计分析进一步揭示了这些阶段间脑电图活动的显著差异，从而阐明了支撑行人道路穿越行为的神经关联与认知动态。随后，我们利用动态时间规整与K近邻算法构建了一个预测性认知模型，并通过数据驱动的滑动窗口方法进行优化。该模型展现出较高的预测准确性，曲线下面积达0.91，表明其能够在行人实际移动前约1秒预判其道路穿越行为。此项研究为新型VRU-车辆交互范式开辟了道路，并标志着向前瞻性生态系统的重要转变。