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Detecting multi-sensor fusion errors in advanced driver-assistance systems

Authors:

Baishakhi RayAuthors Info & Claims

ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 493 - 505

https://doi.org/10.1145/3533767.3534223

Published: 18 July 2022 Publication History

Abstract

Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the uncertainties brought by sensor outputs, they usually leverage multi-sensor fusion (MSF) to fuse the sensor outputs and produce a more reliable understanding of the surroundings. However, MSF cannot completely eliminate the uncertainties since it lacks the knowledge about which sensor provides the most accurate data and how to optimally integrate the data provided by the sensors. As a result, critical consequences might happen unexpectedly. In this work, we observed that the popular MSF methods in an industry-grade ADAS can mislead the car control and result in serious safety hazards. We define the failures (e.g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors. We further apply causality analysis to show that the found fusion errors are indeed caused by the MSF method. We evaluate our framework on two widely used MSF methods in two driving environments. Experimental results show that FusED identifies more than 150 fusion errors. Finally, we provide several suggestions to improve the MSF methods we study.

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Cited By

Malviya VMinn WShar LJiang L(2025)Fuzzing drones for anomaly detection: A systematic literature reviewComputers & Security10.1016/j.cose.2024.104157148(104157)Online publication date: Jan-2025
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Feng SYe YShi QCheng ZXu XCheng SChoi HZhang XFilkov VRay BZhou M(2024)ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695530(1620-1632)Online publication date: 27-Oct-2024
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Index Terms

Detecting multi-sensor fusion errors in advanced driver-assistance systems
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering
    2. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

July 2022

808 pages

ISBN:9781450393799

DOI:10.1145/3533767

General Chair:
Sukyoung Ryu
KAIST, South Korea
,
Program Chair:
Yannis Smaragdakis
University of Athens, Greece

Copyright © 2022 ACM.

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Published: 18 July 2022

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ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

July 18 - 22, 2022

Virtual, South Korea

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Overall Acceptance Rate 58 of 213 submissions, 27%

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Feng SYe YShi QCheng ZXu XCheng SChoi HZhang XFilkov VRay BZhou M(2024)ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695530(1620-1632)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695530
Li FBi RWang JSun JSun ZGuo ZDeng Q(2024)Prioritizing DAGs to Meet Real-Time and Data-Dependency Constraints with Best Efforts2024 IEEE 14th International Symposium on Industrial Embedded Systems (SIES)10.1109/SIES62473.2024.10768040(77-84)Online publication date: 23-Oct-2024
https://doi.org/10.1109/SIES62473.2024.10768040
Wunderle YLyczkowski EHohmann S(2024)Safe object detection in AMRs - a Survey2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE)10.1109/ISIE54533.2024.10595686(1-8)Online publication date: 18-Jun-2024
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Lin SChen FXi LWang GXi RSun YZhu H(2024)TM-fuzzer: fuzzing autonomous driving systems through traffic managementAutomated Software Engineering10.1007/s10515-024-00461-w31:2Online publication date: 27-Jul-2024
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Zhou JTang SGuo YLi YXue Y(2023)From Collision to Verdict: Responsibility Attribution for Autonomous Driving Systems Testing2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE59848.2023.00062(321-332)Online publication date: 9-Oct-2023
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