A Comparative Evaluation of Prominent Methods in Autonomous Vehicle Certification

Computer Vision & MultiModal AI

Published: arXiv: 2511.11484v1

Authors

Mustafa Erdem Kırmızıgül Hasan Feyzi Doğruyol Haluk Bayram

Abstract

The "Vision Zero" policy, introduced by the Swedish Parliament in 1997, aims to eliminate fatalities and serious injuries resulting from traffic accidents. To achieve this goal, the use of self-driving vehicles in traffic is envisioned and a roadmap for the certification of self-driving vehicles is aimed to be determined. However, it is still unclear how the basic safety requirements that autonomous vehicles must meet will be verified and certified, and which methods will be used. This paper focuses on the comparative evaluation of the prominent methods planned to be used in the certification process of autonomous vehicles. It examines the prominent methods used in the certification process, develops a pipeline for the certification process of autonomous vehicles, and determines the stages, actors, and areas where the addressed methods can be applied.

Paper Summary

Problem

The main problem addressed in this research paper is the need for a standardized certification process for autonomous vehicles. As the use of self-driving cars becomes more widespread, ensuring their safety is crucial to achieving the "Vision Zero" policy goal of eliminating fatalities and serious injuries from traffic accidents. However, it is unclear which methods will be used to verify and certify the basic safety requirements of autonomous vehicles.

Key Innovation

This paper innovates by conducting a comparative evaluation of prominent methods used in the certification process of autonomous vehicles, including RSS, STPA, and PEGASUS. The researchers develop a structured pipeline model for the certification process and determine the stages, actors, and areas where these methods can be applied.

Practical Impact

The practical impact of this research is significant, as it provides a roadmap for the certification of autonomous vehicles. The findings of this study can be applied in the real world by policymakers, regulators, and industry stakeholders to ensure the safe deployment of autonomous vehicles. By identifying the most effective methods for certification, this research can help reduce the risk of accidents caused by autonomous vehicles and ultimately contribute to the achievement of the "Vision Zero" goal.

Analogy / Intuitive Explanation

Imagine a complex puzzle where each piece represents a different aspect of autonomous vehicle safety. The certification process is like finding the right combination of pieces to ensure that the entire puzzle is complete and safe to use. The research in this paper helps identify the best methods for finding the right combination of pieces, ensuring that autonomous vehicles are certified and safe for public use.

Paper Information

Categories:

cs.RO cs.CY

Published Date:

arXiv ID:

2511.11484v1

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