Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Maps
Drivable areas and curbs are critical traffic elements for autonomous driving, forming essential components of the vehicle visual perception system and ensuring driving safety. Deep neural networks (DNNs) have significantly improved perception performance for drivable area and curb detection, but most DNN-based methods rely on large manually labeled datasets, which are costly, time-consuming, and expert-dependent, limiting their real-world application. Thus, we developed an automated training data generation module. Our previous work generated training labels using single-frame LiDAR and RGB data, suffering from occlusion and distant point cloud sparsity. In this paper, we propose a novel...
| Authors | Fulong Ma, Daojie Peng, Jun Ma |
|---|---|
| Primary category | cs.CV |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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