Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion
This addresses a critical reliability issue for autonomous driving systems by improving perception under environmental occlusions, though it is incremental as it builds on existing multi-sensor fusion methods.
The paper tackles the problem of camera occlusion degrading multi-view perception in autonomous driving by analyzing its impact on Bird's-Eye View (BEV) vehicle segmentation and using multi-sensor fusion with LiDAR and radar to mitigate it, resulting in significantly enhanced accuracy and robustness.
Autonomous driving technology is rapidly evolving, offering the potential for safer and more efficient transportation. However, the performance of these systems can be significantly compromised by the occlusion on sensors due to environmental factors like dirt, dust, rain, and fog. These occlusions severely affect vision-based tasks such as object detection, vehicle segmentation, and lane recognition. In this paper, we investigate the impact of various kinds of occlusions on camera sensor by projecting their effects from multi-view camera images of the nuScenes dataset into the Bird's-Eye View (BEV) domain. This approach allows us to analyze how occlusions spatially distribute and influence vehicle segmentation accuracy within the BEV domain. Despite significant advances in sensor technology and multi-sensor fusion, a gap remains in the existing literature regarding the specific effects of camera occlusions on BEV-based perception systems. To address this gap, we use a multi-sensor fusion technique that integrates LiDAR and radar sensor data to mitigate the performance degradation caused by occluded cameras. Our findings demonstrate that this approach significantly enhances the accuracy and robustness of vehicle segmentation tasks, leading to more reliable autonomous driving systems.