Title: MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception Show Chinese title

Title: MSC-Bench：多传感器退化驱动感知的基准测试与分析

Abstract: 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. Show Chinese abstract

Abstract: 多传感器融合模型在自动驾驶感知中起着至关重要的作用，特别是在3D目标检测和高清地图构建等任务中。 这些模型为自动驾驶系统提供了关键且全面的静态环境信息。 虽然相机-激光雷达融合方法通过整合两种模态的数据已显示出有希望的结果，但它们通常依赖完整的传感器输入。 这种依赖性可能导致在传感器受损或缺失时鲁棒性降低并出现潜在故障，引发重大的安全问题。 为了解决这一挑战，我们引入了 多传感器损坏基准（MSC-Bench），这是首个旨在评估多传感器自动驾驶感知模型对各种传感器损坏的鲁棒性的综合性基准。 我们的基准包括16种破坏相机和激光雷达输入的损坏类型组合，可以单独或同时发生。 对六种3D目标检测模型和四种高清地图构建模型的广泛评估显示，在恶劣天气条件和传感器故障下性能显著下降，突显了关键的安全问题。 该基准工具包以及相关的代码和模型检查点已公开可用。