融合雷达与激光雷达数据，减轻摄像头遮挡对自动驾驶感知的影响
Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion
- 将多视角摄像头图像投影到鸟瞰图，分析遮挡空间分布
- 在nuScenes数据集上，融合多传感器使车辆分割准确率显著提升
- 适合关注自动驾驶感知鲁棒性的研究者与工程师
自动驾驶技术快速发展，但环境因素如灰尘、雨水和雾气导致的传感器遮挡会严重损害视觉任务性能，包括目标检测、车辆分割和车道识别。本文通过将nuScenes数据集的多视角摄像头图像投影至鸟瞰图（BEV）域，研究各类遮挡对摄像头传感器的影响。该方法揭示了遮挡在BEV空间中的分布特征及其对车辆分割精度的干扰。尽管传感器技术和多传感器融合已有显著进展，现有文献仍缺乏对摄像头遮挡如何影响基于BEV感知系统的具体分析。为此，本文采用融合激光雷达（LiDAR）与雷达（Radar）数据的方法，有效缓解因摄像头遮挡造成的性能下降。实验结果表明，该策略显著提升了车辆分割任务的准确性和系统鲁棒性，增强了自动驾驶系统的可靠性。
原文摘要 · Abstract (English)
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.
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