  In the development of advanced driver-assistance systems (ADAS) and
autonomous vehicles, machine learning techniques that are based on deep neural
networks (DNNs) have been widely used for vehicle perception. These techniques
offer significant improvement on average perception accuracy over traditional
methods, however, have been shown to be susceptible to adversarial attacks,
where small perturbations in the input may cause significant errors in the
perception results and lead to system failure. Most prior works addressing such
adversarial attacks focus only on the sensing and perception modules. In this
work, we propose an end-to-end approach that addresses the impact of
adversarial attacks throughout perception, planning, and control modules. In
particular, we choose a target ADAS application, the automated lane centering
system in OpenPilot, quantify the perception uncertainty under adversarial
attacks, and design a robust planning and control module accordingly based on
the uncertainty analysis. We evaluate our proposed approach using both the
public dataset and production-grade autonomous driving simulator. The
experiment results demonstrate that our approach can effectively mitigate the
impact of adversarial attacks and can achieve 55% to 90% improvement over the
original OpenPilot.
