Ultra-Wideband Technology for Improved Detection of Vulnerable Road Users in Urban Settings: Dataset and Evaluation

Autonomous Vehicles face significant safety challenges in complex urban environments, particularly in detecting and tracking vulnerable road users like pedestrians and cyclists, who are at higher risk of fatal accidents. This paper explores the potential of Ultra-Wideband technology as an additional sensing modality, known for its high ranging accuracy and robustness in challenging environments. Through real-world experiments, we provide a qualitative analysis of Ultra-Wideband performance in scenarios prone to intermittent vision failures, demonstrating its effectiveness in improving vulnerable road users' detection in urban driving scenarios. To enable its widespread application in autonomous driving, we also present WiDEVIEW, the first multimodal dataset that integrates LiDAR, three RGB cameras, GPS/IMU, and Ultra-Wideband sensors for providing urban driving scenarios with extensive pedestrian-vehicle interactions, which can aid in studying pedestrian-vehicle interactions, developing better pedestrian detection and tracking and eventually safe autonomous navigation algorithms by augmenting Ultra-Wideband and using the complimentary properties of Ultra-Wideband sensing with vision and LiDAR data. Finally, we demonstrate the potential applications of the Ultra-Wideband technology in vehicle to vehicle communication and vulnerable road users localization scenarios.