Roadside Sensor Systems for Vulnerable Road User Protection: A Review of Methods and Applications

This review surveyed infrastructure-based sensor technologies for protecting vulnerable road users (VRUs) in traffic safety systems, focusing on four key components of the roadside system: calibration methods, sensor fusion approaches, trajectory prediction, and risk analysis frameworks. The paper analyzes different sensor types, discussing their characteristics, advantages, and limitations. Key challenges in sensor calibration for roadside deployment are addressed, alongside various fusion strategies at data, feature, and decision levels. The review covers trajectory prediction methods, from classical approaches to deep learning architectures, examining their applications in VRU behavior analysis. A comprehensive evaluation of safety assessment methodologies using surrogate safety measures is provided, considering both single and multi-sensor implementations. Technical challenges including scalability, real-time processing, and sensor synchronization are discussed, while identifying opportunities in emerging technologies such as advanced AI and vehicle-to-everything (V2X) integration. The paper concludes by addressing future research directions and ethical implications for improving urban VRU safety.