WiDEVIEW：用于解读行人-车辆交互的超宽带与视觉数据集
WiDEVIEW: An UltraWideBand and Vision Dataset for Deciphering Pedestrian-Vehicle Interactions
AI总结：
本文提出融合 LiDAR、RGB、GPS/IMU 与 UWB 的多模态数据集 WiDEVIEW，用于城市车辆-行人交互研究，并验证 UWB 在视觉失效场景下的定位与测距可靠性。
摘要：
对行人和骑行者等道路使用者进行鲁棒且准确的跟踪与定位，对于确保自动驾驶车辆（AVs）安全、有效导航至关重要，在具有复杂车辆-行人交互的城市驾驶场景中尤其如此。现有用于研究车辆-行人交互的数据集大多以图像为中心，因此容易受到视觉失效的影响。本文研究将超宽带（UWB）作为道路使用者定位的附加模态，以更好地理解车辆-行人交互。我们提出 WiDEVIEW，这是首个融合 LiDAR、三台 RGB 相机、GPS/IMU 和 UWB 传感器的多模态数据集，用于采集城市自动驾驶场景中的车辆-行人交互。数据集以 2D 边界框形式提供真值图像标注，并在标准 2D 目标检测与跟踪算法上进行评估。我们以 LiDAR 作为真值，在视距和非视距条件下的典型交通场景中评估了 UWB 的可行性。结果表明，UWB 测距数据具有与 LiDAR 相当的精度，误差为 0.19 米，并且在视距条件下可在最远 40 米范围内提供可靠的锚点-标签测距数据。非视距条件下的 UWB 性能取决于遮挡物的性质（树木与建筑物）。此外，我们还针对容易出现间歇性视觉失效的场景，对 UWB 性能进行了定性分析。该数据集可通过 https://github.com/unmannedlab/UWB_Dataset 下载。
英文摘要：
Robust and accurate tracking and localization of road users like pedestrians and cyclists is crucial to ensure safe and effective navigation of Autonomous Vehicles (AVs), particularly so in urban driving scenarios with complex vehicle-pedestrian interactions. Existing datasets that are useful to investigate vehicle-pedestrian interactions are mostly image-centric and thus vulnerable to vision failures. In this paper, we investigate Ultra-wideband (UWB) as an additional modality for road users' localization to enable a better understanding of vehicle-pedestrian interactions. We present WiDEVIEW, the first multimodal dataset that integrates LiDAR, three RGB cameras, GPS/IMU, and UWB sensors for capturing vehicle-pedestrian interactions in an urban autonomous driving scenario. Ground truth image annotations are provided in the form of 2D bounding boxes and the dataset is evaluated on standard 2D object detection and tracking algorithms. The feasibility of UWB is evaluated for typical traffic scenarios in both line-of-sight and non-line-of-sight conditions using LiDAR as ground truth. We establish that UWB range data has comparable accuracy with LiDAR with an error of 0.19 meters and reliable anchor-tag range data for up to 40 meters in line-of-sight conditions. UWB performance for non-line-of-sight conditions is subjective to the nature of the obstruction (trees vs. buildings). Further, we provide a qualitative analysis of UWB performance for scenarios susceptible to intermittent vision failures. The dataset can be downloaded via https://github.com/unmannedlab/UWB_Dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
