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Robust and accurate tracking and localization of road users like pedestriansand cyclists is crucial to ensure safe and effective navigation of AutonomousVehicles (AVs), particularly so in urban driving scenarios with complexvehicle-pedestrian interactions. Existing datasets that are useful toinvestigate vehicle-pedestrian interactions are mostly image-centric and thusvulnerable to vision failures. In this paper, we investigate Ultra-wideband(UWB) as an additional modality for road users' localization to enable a betterunderstanding of vehicle-pedestrian interactions. We present WiDEVIEW, thefirst multimodal dataset that integrates LiDAR, three RGB cameras, GPS/IMU, andUWB sensors for capturing vehicle-pedestrian interactions in an urbanautonomous driving scenario. Ground truth image annotations are provided in theform of 2D bounding boxes and the dataset is evaluated on standard 2D objectdetection and tracking algorithms. The feasibility of UWB is evaluated fortypical traffic scenarios in both line-of-sight and non-line-of-sightconditions using LiDAR as ground truth. We establish that UWB range data hascomparable accuracy with LiDAR with an error of 0.19 meters and reliableanchor-tag range data for up to 40 meters in line-of-sight conditions. UWBperformance for non-line-of-sight conditions is subjective to the nature of theobstruction (trees vs. buildings). Further, we provide a qualitative analysisof UWB performance for scenarios susceptible to intermittent vision failures.The dataset can be downloaded via https://github.com/unmannedlab/UWB_Dataset.

WiDEVIEW: An UltraWideBand and Vision Dataset for Deciphering Pedestrian-Vehicle Interactions