ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation

TL;DR

ROCAS is a novel framework for root cause analysis of autonomous driving accidents that combines cyber and physical mutation techniques to accurately identify accident causes and misconfigurations.

Contribution

It introduces a formal ADS root cause analysis framework with cyber-physical co-mutation, addressing challenges unique to ADS environments.

Findings

Effectively narrows down search space for accident causes

Pinpoints specific misconfigurations responsible for accidents

Demonstrates success across 12 accident categories

Abstract

As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is paramount and beneficial for enhancing ADS safety and reliability. Existing cyber-physical system (CPS) root cause analysis techniques are mainly designed for drones and cannot handle the unique challenges introduced by more complex physical environments and deep learning models deployed in ADS. In this paper, we address the gap by offering a formal definition of ADS root cause analysis problem and introducing ROCAS, a novel ADS root cause analysis framework featuring cyber-physical co-mutation. Our technique uniquely leverages both physical and cyber mutation that can precisely identify the accident-trigger entity and pinpoint the misconfiguration of the target ADS responsible for an accident. We further design a differential analysis to identify the responsible module to reduce search space for the misconfiguration. We study 12 categories of ADS accidents and demonstrate the effectiveness and efficiency of ROCAS in narrowing down search space and pinpointing the misconfiguration. We also show detailed case studies on how the identified misconfiguration helps understand rationale behind accidents.