A Systematic Mapping Study on the Debugging of Autonomous Driving Systems

2026-01-07

arXiv.org

AI Summary

This study addresses the lack of systematic research on debugging—encompassing fault localization and repair—in autonomous driving systems, a gap that hinders the assurance of their safety and reliability. For the first time, a Systematic Mapping Study (SMS) methodology is employed to classify, analyze, and synthesize existing literature, offering a comprehensive overview of the current state of research, methodological approaches, and terminology usage in this domain. The analysis reveals significant fragmentation in prior work and identifies critical research gaps. Building on these insights, the paper proposes a unified problem formulation, recommendations for standardizing terminology, and a roadmap for future research, thereby establishing a foundational framework for systematic investigation in autonomous driving system debugging.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As Autonomous Driving Systems (ADS) progress towards commercial deployment, there is an increasing focus on ensuring their safety and reliability. While considerable research has been conducted on testing methods for detecting faults in ADS, very little attention has been paid to debugging in ADS. Debugging is an essential process that follows test failures to localise and repair the faults in the systems to maintain their safety and reliability. This Systematic Mapping Study (SMS) aims to provide a detailed overview of the current landscape of ADS debugging, highlighting existing approaches and identifying gaps in research. The study also proposes directions for future work and standards for problem definition and terminology in the field. Our findings reveal various methods for ADS debugging and highlight the current fragmented yet promising landscape.

Problem

• Autonomous Driving Systems
• Debugging
• Fault Localization
• Systematic Mapping Study
• Safety and Reliability

Innovation

• debugging
• autonomous driving systems
• systematic mapping study
• fault localization
• software reliability