R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception
This dataset addresses the problem of improving safety for Vulnerable Road Users in autonomous driving by providing a comprehensive, multi-sensor resource, though it is incremental as it builds on existing sensor types.
The authors tackled the lack of thermal imaging in roadside perception datasets for autonomous driving by introducing R-LiViT, the first dataset combining LiDAR, RGB, and thermal data from a roadside perspective, resulting in a resource with 10,000 LiDAR frames and 2,400 aligned images across 150 scenarios.
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.