A digital twin of a real intersection can generate LiDAR training data that matches the target location, and a detector trained on it reported 4.8% higher car AP than a model trained on real data, though with more synthetic frames.
R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception
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abstract
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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High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception
A digital twin of a real intersection can generate LiDAR training data that matches the target location, and a detector trained on it reported 4.8% higher car AP than a model trained on real data, though with more synthetic frames.