As mobile robots increasingly operate in environments shared with humans, proactively anticipating human motion rather than responding reactively is critical for preempting collisions during close-proximity navigation, while maintaining mobility efficiency and avoiding unnecessary yields. A timely and motivating engineering application is how autonomous vehicles interpret ambiguous right-of-way such as unsignalized pedestrian crossings. To address this challenge, this study explores the feasibility of decoding preparatory neural activity from wearable electroencephalography (EEG) to predict human motion intention before it is behaviorally expressed. Drawing inspiration from biological predictive coding mechanisms between the sensorimotor cortex and insula-frontoparietal network, we implement this principle in a Temporal Convolutional Network-Transformer architecture to decode fast-evolving EEG signals underlying perception-action transitions. In experiments involving twelve participants simulating road-crossing decisions under varying traffic volume, marked crosswalks, and traffic signals, neurophysiological analyses reveal hemispheric asymmetries in functional specialization and identify high-beta oscillations (16-25 Hz) in the right fronto-central region (F4) as robust neural markers of motor readiness and decision commitment. Using sliding-window feature extraction, we benchmarked sixteen classification models across traditional, recurrent, and convolutional deep learning architectures, and found that our approach achieved the highest Area Under the Curve (AUC) of 0.727 with an approximate 1-second look-ahead. This work demonstrates how biologically grounded temporal architectures can enhance anticipatory intelligence in autonomous systems and represents the first step toward proactive and adaptive human-robot interaction in the built environment.

翻译：随着移动机器人在与人类共享的环境中日益普及，主动预测人体运动而非被动响应对于近距离导航中预防碰撞、维持移动效率及避免不必要的避让至关重要。当前一项紧迫且具有现实意义的工程应用是自动驾驶车辆如何解读无信号灯人行横道等模糊路权场景。为应对这一挑战，本研究探索了通过可穿戴脑电图（EEG）解码预备性神经活动，以在行为表达前预测人类运动意图的可行性。受感觉运动皮层与脑岛-额顶网络间生物预测编码机制的启发，我们将该原理应用于时序卷积网络-Transformer架构中，以解码感知-动作转换过程中快速演变的EEG信号。在12名参与者模拟不同交通流量、斑马线标识及交通信号灯下过马路决策的实验中，神经生理学分析揭示了功能特化的半球不对称性，并发现右侧额中央区（F4）的高频β振荡（16-25 Hz）可作为运动准备与决策确认的稳健神经标记。通过滑动窗口特征提取，我们在传统、循环及卷积深度学习架构中对16种分类模型进行了基准测试，结果表明本方法在约1秒前瞻时间内取得了最高的曲线下面积（AUC）0.727。这项工作展示了基于生物机制的时序架构如何增强自主系统的预见性智能，并为建筑环境中主动自适应的人机交互迈出了第一步。