首个融合激光雷达与热成像的路边感知数据集，专为弱势道路使用者设计。
R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception
- 构建路边视角下的多模态数据集，融合激光雷达、可见光与热成像。
- 包含10,000帧激光雷达数据和2,400对对齐的可见光/热成像图像。
- 聚焦弱势道路使用者，适合自动驾驶感知与多传感器融合研究。
在自动驾驶中，路边感知系统对于克服遮挡问题、提升弱势道路使用者（VRUs）的安全性至关重要。尽管激光雷达和可见光（RGB）传感器被广泛使用，热成像在数据集中仍处于缺失状态，尽管其在极端光照条件下对VRU检测具有显著优势。本文提出R-LiViT，首个从路边视角融合激光雷达、RGB与热成像的数据集，重点服务于VRU感知。R-LiViT在白天与夜间采集了三个路口的数据，涵盖150种交通场景，包含10,000帧激光雷达数据及2,400对时空对齐的RGB与热成像图像，分别标注7类与8类目标，为物体检测与跟踪等任务提供全面资源。数据集及复现评估结果的代码已公开。
原文摘要 · Abstract (English)
In autonomous driving, the integration of roadside perception systems is essential for overcoming occlusion challenges and enhancing the safety of Vulnerable Road Users(VRUs). While LiDAR and visual (RGB) sensors are commonly used, thermal imaging remains underrepresented in datasets, despite its acknowledged advantages for VRU detection in extreme lighting conditions. In this paper, we present R-LiViT, the first dataset to combine LiDAR, RGB, and thermal imaging from a roadside perspective, with a strong focus on VRUs. R-LiViT captures three intersections during both day and night, ensuring a diverse dataset. It includes 10,000 LiDAR frames and 2,400 temporally and spatially aligned RGB and thermal images across 150 traffic scenarios, with 7 and 8 annotated classes respectively, providing a comprehensive resource for tasks such as object detection and tracking. The dataset and the code for reproducing our evaluation results are made publicly available.
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