Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective

2025-11-06 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

Environmental illusions, such as shadows and reflections, are naturally existing yet overlooked phenomena in real-world driving environments that pose serious safety risks to autonomous driving (AD) systems. This work introduces "LanEvil++", the first benchmark for evaluating the robustness of lane perception under these conditions. "LanEvil++" encompasses 14 illusion types, leveraging the CARLA simulator to generate 94 high-fidelity 3D scenes, yielding a dataset of 90,292 annotated images, 1,596 video clips, and 41,855 visual question answering pairs. Evaluations show environmental illusions substantially degrade performance: conventional lane detection (LD) models experience a 5.27% Accuracy and 10.49% F1-score drop, while vision-language models (ADVLMs) see a 2.03% GPT-score and 0.75% Language-score reduction. Shadows are particularly disruptive, reducing LD accuracy by up to 7.20%. The proposed Multimodal Illusion Defense Approach (MIDA) enhances robustness by 4.23% on LD models and 3.82% on ADVLMs, with closed-loop simulations (OpenPilot, LMDrive) and real-world case studies (Jetbot, LIMO PRO) confirming these threats.

Key takeaway

For AI Security Engineers developing autonomous driving systems, you must prioritize robustness testing against environmental illusions like shadows and reflections. These naturally occurring phenomena significantly degrade lane perception, leading to critical safety failures in both conventional and vision-language models. Integrate benchmarks like "LanEvil++" and defense mechanisms such as MIDA to mitigate these risks before real-world deployment.

Key insights

Environmental illusions significantly degrade autonomous driving lane perception, posing critical safety risks.

Principles

• Environmental illusions introduce structurally misleading patterns.
• Robustness evaluation is essential before real-world AD deployment.
• Performance degradation increases with illusion severity.

Method

The Multimodal Illusion Defense Approach (MIDA) uses AAM++ for visual robustness by mixing high/low attention regions from hard examples, and PAT++ for textual robustness via prompt-based adversarial tuning.

In practice

• Use "LanEvil++" to benchmark AD system robustness.
• Implement MIDA's AAM++ for visual defense.
• Apply MIDA's PAT++ for textual robustness in ADVLMs.

Topics

• Autonomous Driving Robustness
• Lane Perception
• Environmental Illusions
• Vision-Language Models
• CARLA Simulator
• LanEvil++ Benchmark
• Multimodal Illusion Defense

Code references

• commaai/openpilot
• NVIDIA-AI-IOT/jetbot
• TuSimple/tusimple-benchmark

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.