Requirements Decomposition for Perception Systems of Autonomous Vehicles: A Case Study of Multi-Object Tracking

Ruilin Yu, Cheng Wang, Zhouhang Lv, Yuxin Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Safety requirements decomposition is critical to ensure a safe autonomous vehicle (AV) by design despite the importance of safety verification and validation. This study proposes a method called QUASARS (QUAntifying SAfety Requirements using Shapley) for efficiently decomposing AV perception safety requirements into component-level and effectively quantifying them. QUASARS models the quantification of the impact of component-level faults on system-level faults as a feature importance calculation problem. We demonstrated QUASARS using a multi-object tracking system as an example and validated component-level safety requirements 100 times on the test set. After meeting the generated component-level safety requirements, the testing system was able to meet the system-level safety requirements, indicating the effectiveness of this method in decomposing system-level safety requirements into component-level.
Original languageEnglish
Title of host publication27th IEEE International Conference on Intelligent Transportation Systems 2024
PublisherIEEE
Pages4095-4101
Number of pages7
ISBN (Electronic)9798331505929
DOIs
Publication statusPublished - 20 Mar 2025

Keywords

  • Quantitative Requirements
  • Risk Decomposition
  • SOTIF
  • Shapely Value
  • autonomous vehicles

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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