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A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems

arXiv 2503.02498 ↗First posted 4 Mar 2025eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

In the automobile industry, ensuring the safety of automated vehicles equipped with the Automated Driving System (ADS) is becoming a significant focus due to the increasing development and deployment of automated driving. Automated driving depends on sensing both the external and internal environments of a vehicle, utilizing perception sensors and algorithms, and Electrical/Electronic (E/E) systems for situational awareness and response. ISO 21448 is the standard for Safety of the Intended Functionality (SOTIF) that aims to ensure that the ADS operate safely within their intended functionality. SOTIF focuses on preventing or mitigating potential hazards that may arise from the limitations or failures of the ADS, including hazards due to insufficiencies of specification, or performance insufficiencies, as well as foreseeable misuse of the intended functionality. However, the challenge lies in ensuring the safety of vehicles despite the limited availability of extensive and systematic literature on SOTIF. To address this challenge, a Systematic Literature Review (SLR) on SOTIF for the ADS is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The objective is to methodically gather and analyze the existing literature on SOTIF. The major contributions of this paper are: (i) presenting a summary of the literature by synthesizing and organizing the collective findings, methodologies, and insights into distinct thematic groups, and (ii) summarizing and categorizing the acknowledged limitations based on data extracted from an SLR of 51 research papers published between 2018 and 2023. Furthermore, research gaps are determined, and future research directions are proposed.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 101 references that are in the map. First posted on arXiv 4 Mar 2025.

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References · 24

  1. 2020Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Taskscited by 31
  2. 2022Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake Systemcited by 14
  3. 2024Engineering Safety Requirements for Autonomous Driving with Large Language Modelscited by 14
  4. 2019Testing and verification of neural-network-based safety-critical control software: A systematic literature reviewcited by 12
  5. 2023Deep Learning Safety Concerns in Automated Driving Perceptioncited by 10
  6. 2021A Review of Testing Object-Based Environment Perception for Safe Automated Drivingcited by 9
  7. 2021Safety of the Intended Driving Behavior Using Rulebookscited by 8
  8. 2022PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarioscited by 8
  9. 2019On the validation of complex systems operating in open contextscited by 6
  10. 2021ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehiclescited by 6
  11. 2022SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Drivingcited by 5
  12. 2023Self-Aware Trajectory Prediction for Safe Autonomous Drivingcited by 5
12 more
  1. 2022Formal Certification Methods for Automated Vehicle Safety Assessmentcited by 4
  2. 2024An Industrial Experience Report about Challenges from Continuous Monitoring, Improvement, and Deployment for Autonomous Driving Featurescited by 3
  3. 2023Systematic Modeling Approach for Environmental Perception Limitations in Automated Drivingcited by 2
  4. 2023Rethinking Certification for Higher Trust and Ethical Safeguarding of Autonomous Systemscited by 2
  5. 2023The Safety Shell: an Architecture to Handle Functional Insufficiencies in Automated Drivingcited by 2
  6. 2021Scenario-Based Safety Assessment Framework for Automated Vehiclescited by 1
  7. 2023State of the Art Study of the Safety Argumentation Frameworks for Automated Driving System Safetycited by 1
  8. 2023Triggering Conditions Analysis and Use Case for Validation of ADAS/ADS Functionscited by 1
  9. 2023On Quantification for SOTIF Validation of Automated Driving Systemscited by 1
  10. 2023Is Scenario Generation Ready for SOTIF? A Systematic Literature Reviewcited by 1
  11. 2023SOTIF-Compliant Scenario Generation Using Semi-Concrete Scenarios and Parameter Samplingcited by 1
  12. 2023Safety of the Intended Functionality Concept Integration into a Validation Tool Suitecited by 1

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