Guardauto: a decentralized runtime protection system for autonomous driving

• Main Authors: Cheng, Kun, Zhou, Yuan, Chen, Bihuan, Wang, Rui, Bai, Yuebin, Liu, Yang
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Autonomous Driving Systems; Self-Adaptive Systems
• Online Access: https://hdl.handle.net/10356/159499
• Institution: Nanyang Technological University

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.