End-to-end uncertainty-based mitigation of adversarial attacks to automated lane centering

Ruochen Jiao, Hengyi Liang, Takami Sato, Junjie Shen, Qi Alfred Chen, Qi Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 public dataset and production-grade autonomous driving simulator. The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attack and can achieve 55% 90% improvement over the original OpenPilot.

Original languageEnglish (US)
Title of host publication32nd IEEE Intelligent Vehicles Symposium, IV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages266-273
Number of pages8
ISBN (Electronic)9781728153940
DOIs
StatePublished - Jul 11 2021
Event32nd IEEE Intelligent Vehicles Symposium, IV 2021 - Nagoya, Japan
Duration: Jul 11 2021Jul 17 2021

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2021-July

Conference

Conference32nd IEEE Intelligent Vehicles Symposium, IV 2021
Country/TerritoryJapan
CityNagoya
Period7/11/217/17/21

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modeling and Simulation

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