A Comprehensive Review on Artificial Intelligence Empowered Solutions for Enhancing Pedestrian and Cyclist Safety
Shucheng Zhang, Yan Shi, Bingzhang Wang, Yuang Zhang, Muhammad Monjurul Karim, Kehua Chen, Chenxi Liu, Mehrdad Nasri, Yinhai Wang
TL;DR
This paper addresses the challenge of ensuring VRU safety in dynamic urban environments through AI-powered, camera-based sensing. It systematically reviews four core vision tasks—detection/classification, tracking/re-identification, trajectory prediction, and intent recognition/prediction—and surveys state-of-the-art methods across monocular and multispectral modalities, single- and multi-camera setups, and both discriminative and generative modeling approaches. Key contributions include a synthesis of techniques enabling proactive VRU protection, a clear articulation of deployment challenges, and actionable directions for data, generalization, edge efficiency, and hardware resilience. The work highlights the practical importance of bridging advanced perception with real-world ITS infrastructure, calling for multimodal, scalable, and fair AI systems that perform reliably under diverse conditions. Overall, the survey provides a foundational reference to guide future research and deployment of AI-driven VRU sensing systems.
Abstract
Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, remains a critical global challenge, as conventional infrastructure-based measures often prove inadequate in dynamic urban environments. Recent advances in artificial intelligence (AI), particularly in visual perception and reasoning, open new opportunities for proactive and context-aware VRU protection. However, existing surveys on AI applications for VRUs predominantly focus on detection, offering limited coverage of other vision-based tasks that are essential for comprehensive VRU understanding and protection. This paper presents a state-of-the-art review of recent progress in camera-based AI sensing systems for VRU safety, with an emphasis on developments from the past five years and emerging research trends. We systematically examine four core tasks, namely detection and classification, tracking and reidentification, trajectory prediction, and intent recognition and prediction, which together form the backbone of AI-empowered proactive solutions for VRU protection in intelligent transportation systems. To guide future research, we highlight four major open challenges from the perspectives of data, model, and deployment. By linking advances in visual AI with practical considerations for real-world implementation, this survey aims to provide a foundational reference for the development of next-generation sensing systems to enhance VRU safety.