InfoResearchPeer-reviewed
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective
- Published
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Summary
This research introduces LanEvil++, the first benchmark for testing lane perception robustness in autonomous driving under environmental illusions such as shadows and tire marks. Built with the CARLA simulator, it covers 14 illusion types across 94 3D scenes, and evaluations show lane detection models lose 5.27% accuracy and 10.49% F1-score, with shadows the most disruptive. The authors also propose the Multimodal Illusion Defense Approach (MIDA), which uses hard examples to improve robustness by 4.23% on lane detection models and 3.82% on ADVLMs.
Mitigation
The authors propose the Multimodal Illusion Defense Approach (MIDA), which uses hard examples to improve illusion resistance, boosting robustness by 4.23% on LD models and 3.82% on ADVLMs.