  Susceptibility of neural networks to adversarial attack prompts serious
safety concerns for lane detection efforts, a domain where such models have
been widely applied. Recent work on adversarial road patches have successfully
induced perception of lane lines with arbitrary form, presenting an avenue for
rogue control of vehicle behavior. In this paper, we propose a modular lane
verification system that can catch such threats before the autonomous driving
system is misled while remaining agnostic to the particular lane detection
model. Our experiments show that implementing the system with a simple
convolutional neural network (CNN) can defend against a wide gamut of attacks
on lane detection models. With a 10% impact to inference time, we can detect
96% of bounded non-adaptive attacks, 90% of bounded adaptive attacks, and 98%
of patch attacks while preserving accurate identification at least 95% of true
lanes, indicating that our proposed verification system is effective at
mitigating lane detection security risks with minimal overhead.
