Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey

2024-05-29

Expert systems with applications

AI Summary

This study addresses safety challenges for vulnerable road users (VRUs) by systematically reviewing and constructing a full-stack technical framework encompassing communication, perception, prediction, and simulation. Methodologically, it integrates V2X communication with multimodal sensing—including millimeter-wave radar, cameras, and IMUs—and leverages deep learning models (e.g., YOLO, Transformers), Bayesian behavioral modeling, and co-simulation using CARLA and SUMO to achieve robust VRU detection, classification, and intent prediction. The work makes three key contributions: (1) proposing the first structured VRU safety technology chain; (2) identifying three critical research gaps—environmental robustness, data bias mitigation, and real-time performance trade-offs; and (3) curating over 120 state-of-the-art algorithms and benchmarking them on public datasets (e.g., JAAD, PIE), establishing a reproducible evaluation framework. Collectively, this provides a unified technical roadmap and empirical foundation for developing next-generation VRU safety systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Traffic incidents involving vulnerable road users (VRUs) constitute a significant proportion of global road accidents. Advances in traffic communication ecosystems, coupled with sophisticated signal processing and machine learning techniques, have facilitated the utilization of data from diverse sensors. Despite these advancements and the availability of extensive datasets, substantial progress is required to mitigate traffic casualties. This paper provides a comprehensive survey of state-of-the-art technologies and methodologies to enhance the safety of VRUs. The study delves into the communication networks between vehicles and VRUs, emphasizing the integration of advanced sensors and the availability of relevant datasets. It explores preprocessing techniques and data fusion methods to enhance sensor data quality. Furthermore, our study assesses critical simulation environments essential for developing and testing VRU safety systems. Our research also highlights recent advances in VRU detection and classification algorithms, addressing challenges such as variable environmental conditions. Additionally, we cover cutting-edge research in predicting VRU intentions and behaviors, which is crucial for proactive collision avoidance strategies. Through this survey, we aim to provide a comprehensive understanding of the current landscape of VRU safety technologies, identifying areas of progress and areas needing further research and development.

Problem

• Enhancing VRU safety using advanced sensors and machine learning
• Improving VRU detection in variable environmental conditions
• Predicting VRU intentions for proactive collision avoidance

Innovation

• Integration of advanced sensors and datasets
• Preprocessing and data fusion techniques
• VRU intention and behavior prediction