  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/.
