Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911693885407232 |
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| author | Zhang, Zhiyuan Jin, Zhenghao Peng, Yanlun Guo, Xianda Liu, Haoran Zhang, Shaofeng Ma, Xingjun Wu, Zuxuan Yan, Junchi Jia, Xiaosong Jiang, Yu-Gang |
| author_facet | Zhang, Zhiyuan Jin, Zhenghao Peng, Yanlun Guo, Xianda Liu, Haoran Zhang, Shaofeng Ma, Xingjun Wu, Zuxuan Yan, Junchi Jia, Xiaosong Jiang, Yu-Gang |
| contents | Robustness is a critical requirement for deploying autonomous driving systems in the real world. Existing robustness benchmarks for autonomous driving have made important progress in studying the effects of image-level corruptions, such as adverse weather or camera degradation, on perception modules and open-loop planning outputs. However, deployment can also involve system-level imperfections, such as inference latency and ego-state estimation errors, which remain less studied in closed-loop E2E-AD evaluation. These imperfections can accumulate through the feedback loop and destabilize control. In this work, we present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving under realistic deployment perturbations. We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures (frame drop, partial observation), ego-state estimation errors (GPS noise, and speed or odometry errors), and compute-induced control delay (model inference delay). We evaluate representative end-to-end driving methods and analyze their robustness under different perturbation severities. Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance, revealing robustness challenges that are not fully captured by conventional image-level corruption evaluations. By establishing a closed-loop evaluation protocol and demonstrating the substantial impact of these deployment-oriented perturbations, Bench2Drive-Robust defines practical robustness problems for end-to-end autonomous driving and encourages further research on deployment-aware robust driving systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18059 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations Zhang, Zhiyuan Jin, Zhenghao Peng, Yanlun Guo, Xianda Liu, Haoran Zhang, Shaofeng Ma, Xingjun Wu, Zuxuan Yan, Junchi Jia, Xiaosong Jiang, Yu-Gang Robotics Robustness is a critical requirement for deploying autonomous driving systems in the real world. Existing robustness benchmarks for autonomous driving have made important progress in studying the effects of image-level corruptions, such as adverse weather or camera degradation, on perception modules and open-loop planning outputs. However, deployment can also involve system-level imperfections, such as inference latency and ego-state estimation errors, which remain less studied in closed-loop E2E-AD evaluation. These imperfections can accumulate through the feedback loop and destabilize control. In this work, we present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving under realistic deployment perturbations. We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures (frame drop, partial observation), ego-state estimation errors (GPS noise, and speed or odometry errors), and compute-induced control delay (model inference delay). We evaluate representative end-to-end driving methods and analyze their robustness under different perturbation severities. Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance, revealing robustness challenges that are not fully captured by conventional image-level corruption evaluations. By establishing a closed-loop evaluation protocol and demonstrating the substantial impact of these deployment-oriented perturbations, Bench2Drive-Robust defines practical robustness problems for end-to-end autonomous driving and encourages further research on deployment-aware robust driving systems. |
| title | Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.18059 |