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- Title
RobustE2E : Exploring the Robustness of End-to-End Autonomous Driving.
- Authors
Jiang, Wei; Wang, Lu; Zhang, Tianyuan; Chen, Yuwei; Dong, Jian; Bao, Wei; Zhang, Zichao; Fu, Qiang
- Abstract
Autonomous driving technology has advanced significantly with deep learning, but noise and attacks threaten its real-world deployment. While research has revealed vulnerabilities in individual intelligent tasks, a comprehensive evaluation of these impacts across complete end-to-end systems is still underexplored. To address this void, we thoroughly analyze the robustness of four end-to-end autonomous driving systems against various noise and build the RobustE2E Benchmark, including five traditional adversarial attacks and a newly proposed Module-Wise Attack specifically targeting end-to-end autonomous driving in white-box settings, as well as four major categories of natural corruptions (a total of 17 types, with five severity levels) in black-box settings. Additionally, we extend the robustness evaluation from the open-loop model level to the closed-loop case studies of autonomous driving system level. Our comprehensive evaluation and analysis provide valuable insights into the robustness of end-to-end autonomous driving, which may offer potential guidance for targeted improvements to models. For example, (1) even the most advanced end-to-end models suffer large planning failures under minor perturbations, with perception tasks showing the most substantial decline; (2) among adversarial attacks, our Module-Wise Attack poses the greatest threat to end-to-end autonomous driving models, while PGD- l 2 is the weakest, and among four categories of natural corruptions, noise and weather are the most harmful, followed by blur and digital distortion being less severe; (3) the integrated, multitask approach results in significantly higher robustness and reliability compared with the simpler design, highlighting the critical role of collaborative multitask in autonomous driving; and (4) the autonomous driving systems amplify the model's lack of robustness, etc. Our research contributes to developing more resilient autonomous driving models and their deployment in the real world.
- Subjects
AUTONOMOUS vehicles; DEEP learning; CORRUPTION; NOISE; WEATHER
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 16, p3299
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13163299