LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions

TL;DR

This paper systematically design 14 prevalent yet critical types of environmental illusions that cover a wide spectrum of real-world influencing factors in LD tasks and establishes the first comprehensive benchmark LanEvil for evaluating the robustness of LD against this natural corruption.

Abstract

Lane detection (LD) is an essential component of autonomous driving systems, providing fundamental functionalities like adaptive cruise control and automated lane centering. Existing LD benchmarks primarily focus on evaluating common cases, neglecting the robustness of LD models against environmental illusions such as shadows and tire marks on the road. This research gap poses significant safety challenges since these illusions exist naturally in real-world traffic situations. For the first time, this paper studies the potential threats caused by these environmental illusions to LD and establishes the first comprehensive benchmark LanEvil for evaluating the robustness of LD against this natural corruption. We systematically design 14 prevalent yet critical types of environmental illusions (e.g., shadow, reflection) that cover a wide spectrum of real-world influencing factors in LD tasks. Based on real-world environments, we create 94 realistic and customizable 3D cases using the widely used CARLA simulator, resulting in a dataset comprising 90,292 sampled images. Through extensive experiments, we benchmark the robustness of popular LD methods using LanEvil, revealing substantial performance degradation (-5.37% Accuracy and -10.70% F1-Score on average), with shadow effects posing the greatest risk (-7.39% Accuracy). Additionally, we assess the performance of commercial auto-driving systems OpenPilot and Apollo through collaborative simulations, demonstrating that proposed environmental illusions can lead to incorrect decisions and potential traffic accidents. To defend against environmental illusions, we propose the Attention Area Mixing (AAM) approach using hard examples, which witness significant robustness improvement (+3.76%) under illumination effects. We hope our paper can contribute to advancing more robust auto-driving systems in the future. Website: https://lanevil.github.io/.

Figures (9)

• Figure 1: Illustration of naturally existing yet overlooked environmental illusions (e.g., shadow). Perception of these patterns that objectively exist in such a way could cause misinterpretation of their actual nature leading to wrong lane recognition.
• Figure 2: The framework of our LanEvil benchmark, which contains 14 specially-designed environmental illusion types from 4 categories including road damage, traffic obstruction, reflection, and shadow.
• Figure 3: Illustration of our data collection pipeline.
• Figure 4: The statistics of LanEvil dataset. (a) The number of original and perturbed cases under four categories. (b) The case distribution of four illusion categories.
• Figure 5: Visualization of images from our LanEvil dataset under different environmental illusions.