Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective
Summary
Environmental illusions like shadows and tire marks can trick autonomous driving systems into misreading lanes, a safety problem that hasn't been studied much until now. Researchers created LanEvil++, a benchmark (a test suite for measuring performance) with 90,000+ images showing 14 types of illusions to evaluate how well lane detection models handle these challenges. The study found that shadows cause the most problems, reducing model accuracy by 5-10%, and proposes the Multimodal Illusion Defense Approach (MIDA, a training method using difficult examples) to improve robustness.
Solution / Mitigation
The source proposes the Multimodal Illusion Defense Approach (MIDA), which uses hard examples to improve illusion resistance. According to the text, 'MIDA achieves substantial gains under challenging conditions, boosting robustness by 4.23% on LD models and 3.82% on ADVLMs.'
Classification
Related Issues
Original source: http://ieeexplore.ieee.org/document/11595247
First tracked: September 14, 2026 at 08:04 PM
Classified by LLM (prompt v3) · confidence: 92%