Visible to the public BenchAV: A Security Benchmarking Framework for Autonomous Driving

TitleBenchAV: A Security Benchmarking Framework for Autonomous Driving
Publication TypeConference Paper
Year of Publication2022
AuthorsHoque, Mohammad Aminul, Hossain, Mahmud, Hasan, Ragib
Conference Name2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)
Date Publishedjan
Keywordsautonomous driving, autonomous vehicle, Benchmark testing, Benchmarking, Measurement, Metrics, Operating systems, Pressing, pubcrawl, Roads, Robot Operating System Security, Safety, security, security metrics, Software
Abstract

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV - a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

DOI10.1109/CCNC49033.2022.9700548
Citation Keyhoque_benchav_2022