Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159499
Title: Guardauto: a decentralized runtime protection system for autonomous driving
Authors: Cheng, Kun
Zhou, Yuan
Chen, Bihuan
Wang, Rui
Bai, Yuebin
Liu, Yang
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Cheng, K., Zhou, Y., Chen, B., Wang, R., Bai, Y. & Liu, Y. (2020). Guardauto: a decentralized runtime protection system for autonomous driving. IEEE Transactions On Computers, 70(10), 1569-1581. https://dx.doi.org/10.1109/TC.2020.3018329
Project: NRF2014NCRNCR001-30
NRF2018NCR-NCR005-0001
NSOE003-0001
NRFI06-2020-0022
Journal: IEEE Transactions on Computers
Abstract: Due to the broad attack surface and the lack of runtime protection, potential safety and security threats hinder the real-life adoption of autonomous vehicles. Although efforts have been made to mitigate some specific attacks, there are few works on the protection of the autonomous driving system, i.e., the control software system performing such as perception, decision making, and motion tracking. This article presents a decentralized self-protection framework called Guardauto to protect the autonomous driving system against runtime threats. First, Guardauto proposes an isolation model to decouple the autonomous driving system and isolate its components with a set of partitions. Second, Guardauto provides self-protection mechanisms for each target component, which combines different methods to monitor the target execution and plan adaption actions accordingly. Third, Guardauto provides cooperation among local self-protection mechanisms to identify the root-cause component in the case of cascading failures affecting multiple components. A prototype has been implemented and evaluated on the open-source autonomous driving system Autoware. Results show that Guardauto could effectively mitigate runtime failures and attacks, and protect the control system with acceptable performance overhead.
URI: https://hdl.handle.net/10356/159499
ISSN: 0018-9340
DOI: 10.1109/TC.2020.3018329
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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