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Quantitative ASIL Estimation Using Fuzzy Set Theory

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Abstract

The present study proposes a method that numerically quantifies automotive safety integrity level (ASIL). Additionally, the method reduces the uncertainty in the risk estimation step by subdividing the ASIL step by applying fuzzy theory to reduce the ambiguity of ASIL with an extremely wide step. The conventional risk-based design concept is adopted to quantify ASIL, and fuzzy modeling is used to express the vehicle safety integrity ratings of uncertain automotive electrical equipment. To overcome the uncertainty, we fuzzify the variable using the mean expected value method and select the trapezoidal fuzzy number. The final fuzzy output is generated using Mamdani’s method with 25 fuzzy rules combined with variables to obtain risk. This theory provides a useful approach for overcoming the inherent uncertainty in ASIL. We attempt to establish the effectiveness of the proposed method by utilizing it to quantify the automatic emergency-braking device scenario, which is a typical target system.

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Abbreviations

AEB:

Autonomous Emergency Braking

LKAS:

Lane Keeping Assist System

ECUs:

Electronic Control Units

ASIL:

Automotive Safety Integrity Level

HAZOP:

Hazard and Operability Analysis

ECAP:

European New Car Assessment

VRU:

Vulnerable Road User

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Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07042731).

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Authors and Affiliations

  1. Department of Information and Industrial Engineering, Yonsei University, Seoul, 03722, Korea

    Sung min Kim

  2. Department of Convergence Standard, Dongguk University, Seoul, 04620, Korea

    Gyeong-hun Do

  3. Plant Engineering Center, Institute for Advanced Engineering (IAE), Goan-ro 51, Baegam-myeon, Gyeonggi-do, Yongin, 17180, Korea

    Junkeon Ahn

  4. Industrial Convergence Technology Center, Korea Testing Laboratory, Digital-ro 26-gil, Guro-gu, Seoul, 08389, Korea

    Juneyoung Kim

Authors
  1. Sung min Kim
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  2. Gyeong-hun Do
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  3. Junkeon Ahn
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  4. Juneyoung Kim
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Correspondence to Juneyoung Kim.

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Kim, S.m., Do, Gh., Ahn, J. et al. Quantitative ASIL Estimation Using Fuzzy Set Theory. Int.J Automot. Technol. 21, 1177–1184 (2020). https://doi.org/10.1007/s12239-020-0111-y

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  • DOI: https://doi.org/10.1007/s12239-020-0111-y

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