A Systematic Mapping Study on the Debugging of Autonomous Driving Systems

TL;DR

This paper provides a comprehensive overview of current debugging approaches for Autonomous Driving Systems, highlighting research gaps and proposing future directions to enhance safety and reliability.

Contribution

It systematically maps existing ADS debugging methods, identifies research gaps, and suggests standardization and future research directions.

Findings

Various ADS debugging methods identified

Fragmented research landscape highlighted

Guidelines for future research proposed

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.