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

arXiv: https://arxiv.org/abs/2409.07774 · HTML: https://mlsanity.com/paper/2409.07774/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 91 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [DriveFuzz: Discovering Autonomous Driving Bugs through Driving Quality-Guided Fuzzing](https://mlsanity.com/paper/2211.01829/) (2022)
- [MDPFuzz: Testing Models Solving Markov Decision Processes](https://mlsanity.com/paper/2112.02807/) (2021)
- [Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks](https://mlsanity.com/paper/2201.04610/) (2022)
- [Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems](https://mlsanity.com/paper/2109.06404/) (2021)
- [On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support](https://mlsanity.com/paper/2203.10356/) (2022)
- [A Search-Based Framework for Automatic Generation of Testing Environments for Cyber-Physical Systems](https://mlsanity.com/paper/2203.12138/) (2022)
- [CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots](https://mlsanity.com/paper/2301.07690/) (2023)
- [Control Parameters Considered Harmful: Detecting Range Specification Bugs in Drone Configuration Modules via Learning-Guided Search](https://mlsanity.com/paper/2112.03511/) (2021)
- [Automated Misconfiguration Repair of Configurable Cyber-Physical Systems with Search: an Industrial Case Study on Elevator Dispatching Algorithms](https://mlsanity.com/paper/2301.01487/) (2023)
- [ACAV: A Framework for Automatic Causality Analysis in Autonomous Vehicle Accident Recordings](https://mlsanity.com/paper/2401.07063/) (2024)
- [Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing](https://mlsanity.com/paper/2407.04359/) (2024)
- [Trace-Checking CPS Properties: Bridging the Cyber-Physical Gap](https://mlsanity.com/paper/2009.12250/) (2020)
- [Helion: Enabling Natural Testing of Smart Homes](https://mlsanity.com/paper/2308.06695/) (2023)

## Built on by (most cited first, 3 of 3)

- [TAI3: Testing Agent Integrity in Interpreting User Intent](https://mlsanity.com/paper/2506.07524/) (2025)
- [RFCAudit: An LLM Agent for Functional Bug Detection in Network Protocols](https://mlsanity.com/paper/2506.00714/) (2025)
- [Importance Sampling and PCA for Finding Failures in Commercial Autonomous Vehicles](https://mlsanity.com/paper/2607.18106/) (2026)

## Abstract (arXiv)

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.