A Comprehensive Review on Artificial Intelligence Empowered Solutions for Enhancing Pedestrian and Cyclist Safety

2025-09-30

AI Summary

To address insufficient safety protection for vulnerable road users (VRUs)—including pedestrians and cyclists—in dynamic urban environments, this paper presents a systematic review of camera-based visual AI techniques applied to VRU safety from 2019 to 2024. We focus on four core perception tasks: detection and classification, multi-object tracking and re-identification, trajectory prediction, and intent recognition. For the first time, we propose a comprehensive, full-stack visual perception framework specifically tailored to VRU safety. Our analysis identifies four persistent open challenges: (1) scarcity of high-quality, diverse VRU datasets; (2) limited model generalizability across heterogeneous urban scenes; (3) poor cross-scenario robustness under varying lighting, occlusion, and viewpoint conditions; and (4) difficulties in efficient edge deployment due to computational and latency constraints. The work establishes a foundational theoretical basis, outlines a principled technical roadmap, and delivers an authoritative research guide for proactive VRU safety assurance in intelligent transportation systems.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• Addressing inadequate conventional safety measures for pedestrians and cyclists
• Expanding AI applications beyond detection to comprehensive VRU protection tasks
• Bridging visual AI advances with real-world implementation challenges

Innovation

• Camera-based AI sensing for VRU safety
• Systematic review of four core vision tasks
• Linking visual AI advances with real-world deployment