MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；IQIYI（爱奇艺）；Institute of Automation CAS（中国科学院自动化研究所）；Samsung（三星）；The University of Sydney（悉尼大学）；National University of Singapore（新加坡国立大学）；Institute of Information Engineering CAS（中国科学院信息工程研究所）；Hefei University of Technology（合肥工业大学）
Xiaoshuai Hao, Guanqun Liu, Yuting Zhao, Yuheng Ji, Mengchuan Wei, Haimei Zhao, Lingdong Kong, Rong Yin, Yu Liu
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.
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
National University of Singapore（新加坡国立大学）；Huazhong University of Science and Technology（华中科技大学）；Carnegie Mellon University（卡内基梅隆大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；IPAL, CNRS IRL 2955（法国国家科学研究中心国际联合实验室2955（IPAL））；CerCo, CNRS UMR 5549, Université Toulouse III（图卢兹第三大学法国国家科学研究中心混合研究单位5549（CerCo））
NavTrust: Benchmarking Trustworthiness for Embodied Navigation
Trustworthy Autonomous Systems Laboratory at the University of California, Riverside（加州大学河滨分校可信自主系统实验室）；University of Michigan（密歇根大学）；Workday；University of Southern California（南加州大学）；Texas A&M University（德克萨斯A&M大学）；Lehigh University（莱斯大学）
RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions
Huazhong University of Science and Technology（华中科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions
School of Digital and Intelligent Industry (School of Cyber Science and Technology)（数字与智能产业学院（网络科学与技术学院））；Inner Mongolia University of Science and Technology（内蒙古科技大学）
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；Kyushu University（九州大学）；Nanyang Technological University（南洋理工大学）；New York University（纽约大学）
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
Shanghai AI Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；Hochschule Bremerhaven（不来梅哈芬应用技术大学）；East China Normal University（华东师范大学）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）
