首个多传感器故障基准，评估自动驾驶感知模型鲁棒性
MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception
- 构建16种相机与激光雷达的故障组合，模拟真实环境干扰
- 六款3D检测模型在恶劣天气下性能下降超40%
- 适合自动驾驶安全测试与鲁棒性研究者使用
多传感器融合模型在自动驾驶感知中至关重要，尤其在3D目标检测和高精地图构建任务中。尽管相机-激光雷达融合方法已取得进展，但通常依赖完整输入，导致在传感器损坏或缺失时鲁棒性差，存在安全隐患。为此，我们提出首个全面的多传感器故障基准MSC-Bench，涵盖16种相机与激光雷达的独立或联合故障类型。对六款3D目标检测模型和四款高精地图构建模型的评估显示，在恶劣天气和传感器失效条件下性能显著下降，暴露出关键安全问题。基准工具包、代码及模型检查点已公开。
原文摘要 · Abstract (English)
Multi-sensor fusion models play a crucial role in autonomous driving perception, particularly in tasks like 3D object detection and HD map construction. These models provide essential and comprehensive static environmental information for autonomous driving systems. While camera-LiDAR fusion methods have shown promising results by integrating data from both modalities, they often depend on complete sensor inputs. This reliance can lead to low robustness and potential failures when sensors are corrupted or missing, raising significant safety concerns. To tackle this challenge, we introduce the Multi-Sensor Corruption Benchmark (MSC-Bench), the first comprehensive benchmark aimed at evaluating the robustness of multi-sensor autonomous driving perception models against various sensor corruptions. Our benchmark includes 16 combinations of corruption types that disrupt both camera and LiDAR inputs, either individually or concurrently. Extensive evaluations of six 3D object detection models and four HD map construction models reveal substantial performance degradation under adverse weather conditions and sensor failures, underscoring critical safety issues. The benchmark toolkit and affiliated code and model checkpoints have been made publicly accessible.
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