从车道感知角度评估自动驾驶对环境错觉的鲁棒性：基准测试
Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective
AI总结：
研究自动驾驶对环境错觉的鲁棒性，聚焦传统车道检测和视觉语言模型系统。引入基准LanEvil++并评估，发现错觉严重影响性能，阴影干扰最大。提出多模态错觉防御方法MIDA，有效提升了模型在挑战性条件下的鲁棒性。
摘要：
环境错觉（如阴影、反射和轮胎痕迹）在现实驾驶环境中自然存在但被忽视，会干扰视觉感知，给自动驾驶系统带来严重安全风险。现有研究大多忽略此现象。本文从车道感知角度研究自动驾驶鲁棒性，聚焦传统车道检测和基于视觉语言模型的系统。引入首个基准LanEvil++，涵盖14种错觉，利用CARLA模拟器生成数据集。评估表明错觉严重降低现有方法性能，阴影影响最大。闭环模拟和实际案例显示会导致错误驾驶决策。为此提出多模态错觉防御方法（MIDA），在挑战性条件下提升了鲁棒性。
英文摘要：
Environmental illusions (eg., shadows, reflections, and tire marks) are naturally existing yet overlooked phenomena in real-world driving environments. They can disturb visual perception, leading to misinterpretation of the scene and posing serious safety risks to autonomous driving (AD) systems. However, existing researches largely overlook these phenomena, leaving a critical gap. To address this issue, we study AD robustness through the lane perception perspective, a fundamental task supporting core functions like cruise control and lane centering. We focus on two representative models: conventional lane detection (LD) and vision-language model-based systems (ADVLMs). In this work, we introduce the first benchmark, LanEvil++, for evaluating the robustness of lane perception under environmental illusions. LanEvil++ encompasses 14 types of illusions and leverages the CARLA simulator to generate 94 high-fidelity, fully controllable 3D scenes, yielding a dataset of 90,292 annotated images, 1,596 video clips, and 41,855 visual question answering pairs. Extensive evaluations demonstrate that environmental illusions substantially degrade the performance of state-of-the-art LD methods. On average, LD models experience a 5.27% drop in Accuracy and a 10.49% decline in F1-score, while ADVLMs show a 2.03% reduction in GPT-score and a 0.75% drop in Language-score. Among all illusions, shadows emerge as the most disruptive factor, reducing accuracy by up to 7.20%. Furthermore, closed-loop simulations reveal that these illusions can lead to incorrect driving decisions. Complementary real-world case studies highlight safety-critical failures in actual traffic scenes. To enhance robustness, we propose the Multimodal Illusion Defense Approach (MIDA). MIDA achieves substantial gains under challenging conditions, boosting robustness by 4.23% on LD models and 3.82% on ADVLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
