预测行人过街意图，提升路口交通安全
VRU-CIPI: Crossing Intention Prediction at Intersections for Improving Vulnerable Road Users Safety
- 用GRU和多头自注意力捕捉行人动作与环境依赖关系
- 在UCF-VRU数据集上达96.45%准确率，推理速度33帧/秒
- 可与车路协同系统结合，提前预警并优化信号控制
理解真实世界中城市交叉口的人类行为对提升道路使用者交互安全至关重要。其中，弱势道路使用者（VRUs）的过街意图预测尤为关键，误判可能导致与来车发生危险冲突。本文提出基于序列注意力机制的VRU-CIPI框架，利用门控循环单元（GRU）捕捉行人运动的时间动态，结合多头Transformer自注意力机制编码影响过街方向的关键上下文与空间依赖关系。在UCF-VRU数据集上的实验表明，该方法达到96.45%的准确率，并实现每秒33帧的实时推理速度。进一步地，通过与车路协同（I2V）通信集成，可主动触发过街信号并提前向联网车辆发送预警，从而保障所有道路使用者更顺畅、更安全的交互。
原文摘要 · Abstract (English)
Understanding and predicting human behavior in-thewild, particularly at urban intersections, remains crucial for enhancing interaction safety between road users. Among the most critical behaviors are crossing intentions of Vulnerable Road Users (VRUs), where misinterpretation may result in dangerous conflicts with oncoming vehicles. In this work, we propose the VRU-CIPI framework with a sequential attention-based model designed to predict VRU crossing intentions at intersections. VRU-CIPI employs Gated Recurrent Unit (GRU) to capture temporal dynamics in VRU movements, combined with a multi-head Transformer self-attention mechanism to encode contextual and spatial dependencies critical for predicting crossing direction. Evaluated on UCF-VRU dataset, our proposed achieves state-of-the-art performance with an accuracy of 96.45% and achieving real-time inference speed reaching 33 frames per second. Furthermore, by integrating with Infrastructure-to-Vehicles (I2V) communication, our approach can proactively enhance intersection safety through timely activation of crossing signals and providing early warnings to connected vehicles, ensuring smoother and safer interactions for all road users.
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