VRU-CIPI: Crossing Intention Prediction at Intersections for Improving Vulnerable Road Users Safety

TL;DR

This paper introduces VRU-CIPI, a novel attention-based model that accurately predicts crossing intentions of vulnerable road users at intersections, enhancing safety through real-time inference and integration with vehicle communication systems.

Contribution

The paper presents a new sequential attention-based framework combining GRU and Transformer mechanisms for VRU crossing prediction, achieving state-of-the-art accuracy and real-time performance.

Findings

Achieved 96.45% accuracy on UCF-VRU dataset.

Real-time inference at 33 frames per second.

Enhanced intersection safety through proactive signals and warnings.

Abstract

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.