ISO/DPAS 8800
(Main)Road vehicles — Safety and artificial intelligence
Road vehicles — Safety and artificial intelligence
This document defines safety-related properties and risk factors impacting the insufficient performance and malfunctioning behaviour of Artificial Intelligence (AI) within a road vehicle context. It describes a framework that addresses all phases of the development and deployment lifecycle. This includes the derivation of suitable safety requirements on the function, considerations related to data quality and completeness, architectural measures for the control and mitigation of failures, tools used to support AI, verification and validation techniques as well as the evidence required to support an assurance argument for the overall safety of the system.
Véhicules routiers — Sécurité et intelligence artificielle
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ISO/TC 22/SC 32
Road vehicles — Safety and artificial
Secretariat: JISC
intelligence
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Véhicules routiers — Sécurité et intelligence artificielle 2024-08-21
Voting terminates on:
2024-10-16
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TO BECOME STAN DARDS TO WHICH REFERENCE MAY BE
MADE IN NATIONAL REGULATIONS.
Reference number
FINAL DRAFT
Publicly
Available
Specification
ISO/TC 22/SC 32
Road vehicles — Safety and artificial
Secretariat: JISC
intelligence
Voting begins on:
Véhicules routiers — Sécurité et intelligence artificielle
Voting terminates on:
RECIPIENTS OF THIS DRAFT ARE INVITED TO SUBMIT,
WITH THEIR COMMENTS, NOTIFICATION OF ANY
RELEVANT PATENT RIGHTS OF WHICH THEY ARE AWARE
AND TO PROVIDE SUPPOR TING DOCUMENTATION.
© ISO 2024
IN ADDITION TO THEIR EVALUATION AS
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
BEING ACCEPTABLE FOR INDUSTRIAL, TECHNO
LOGICAL, COMMERCIAL AND USER PURPOSES, DRAFT
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
INTERNATIONAL STANDARDS MAY ON OCCASION HAVE
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
TO BE CONSIDERED IN THE LIGHT OF THEIR POTENTIAL
or ISO’s member body in the country of the requester.
TO BECOME STAN DARDS TO WHICH REFERENCE MAY BE
MADE IN NATIONAL REGULATIONS.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland Reference number
ii
Contents Page
Foreword .vi
Introduction .vii
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 2
3.1 General AI-related definitions .2
3.2 Data-related definitions .7
3.3 General safety-related definitions .9
3.4 Safety: Root cause-, error-and failure-related definitions .11
3.5 Miscellaneous definitions . 12
4 Abbreviated terms . 14
5 Requirements for conformity .15
5.1 Purpose . 15
5.2 General requirements . 15
6 AI within the context of road vehicles system safety engineering and basic concepts .16
6.1 Application of the ISO 26262 series for the development of AI systems .16
6.2 Interactions with encompassing system-level safety activities .17
6.3 Mapping of abstraction layers between the ISO 26262 series, ISO/IEC 22989 and this
document . 20
6.4 Example architecture for an AI system . 22
6.5 Types of AI models . 23
6.6 AI technologies of a ML model . 23
6.7 Error concepts, fault models and causal models .24
6.7.1 Cause-and-effect chain . .24
6.7.2 Root cause classes . 26
6.7.3 Error classification based on the safety impact .27
7 AI safety management . .28
7.1 Objectives . 28
7.2 Prerequisites and supporting information . 28
7.3 General requirements . 28
7.4 Reference AI safety life cycle .31
7.5 Iterative development paradigms for AI systems . 33
7.6 Work products . 34
8 Assurance arguments for AI systems .35
8.1 Objectives . 35
8.2 Prerequisites and supporting information . 35
8.3 General requirements . 36
8.4 AI system-specific considerations in assurance arguments . 36
8.5 Structuring assurance arguments for AI systems .37
8.5.1 Context of the assurance argument.37
8.5.2 Categories of evidence . 38
8.6 The role of quantitative targets and qualitative arguments . 39
8.7 Evaluation of the assurance argument . 40
8.8 Work products .41
9 Derivation of AI safety requirements . 41
9.1 Objectives .41
9.2 Prerequisites and supporting information .42
9.3 General requirements .42
9.4 General workflow for deriving safety requirements .43
9.5 Deriving AI safety requirements on supervised machine learning . 46
9.5.1 The need for refined AI safety requirements . 46
iii
9.5.2 Derivation of refined AI safety requirements to manage uncertainty .47
9.5.3 Refinement of the input space definition for AI safety lifecycle . 50
9.5.4 Restricting the occurrence of AI output insufficiencies . 50
9.5.5 Metrics, measurements and threshold design . 54
9.5.6 Considerations for deriving safety requirements . 55
9.6 Work products . 56
10 Selection of AI technologies, architectural and development measures .56
10.1 Objectives . 56
10.2 Prerequisites . 56
10.3 General requirements . 56
10.4 Architecture and development process design or refinement .57
10.5 Examples of architectural and development measures for AI systems . 58
10.6 Work products .62
11 Data-related considerations .62
11.1 Objectives .62
11.2 Prerequisites and supporting information .62
11.3 General requirements .
...
ISO/TC 22/SC 32
ISO/CD PAS 8800(en)
Secretariat: JISC
Date: 2024-08-06
Road vehicles — Safety and artificial intelligence
Véhicules routiers — Sécurité et intelligence artificielle
ISO/CD PASDPAS 8800:2024(:(en)
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication
may be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying,
or posting on the internet or an intranet, without prior written permission. Permission can be requested from either ISO
at the address below or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: + 41 22 749 01 11
E-mail: copyright@iso.org
Website: www.iso.org
Published in Switzerland
ii
ISO/CD PASDPAS 8800:2024(:(en)
Contents
Foreword . viii
Introduction . ix
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 2
3.1 General AI-related definitions . 2
3.2 Data-related definitions . 8
3.3 General safety-related definitions . 9
3.4 Safety: Root cause-, error-and failure-related definitions . 12
3.5 Miscellaneous definitions . 13
4 Abbreviated terms . 15
5 Requirements for conformity . 16
5.1 Purpose . 16
5.2 General requirements . 16
6 AI within the context of road vehicles system safety engineering and basic concepts. 17
6.1 Application of the ISO 26262 series for the development of AI systems . 17
6.2 Interactions with encompassing system-level safety activities . 18
6.3 Mapping of abstraction layers between ISO 26262, ISO/IEC 22989 and this document . 22
6.4 Example architecture for an AI system . 25
6.5 Types of AI models . 26
6.6 AI technologies of a ML model . 26
6.7 Error concepts, fault models and causal models . 27
6.7.1 Cause-and-effect chain . 27
6.7.2 Root cause classes . 28
6.7.3 Error classification based on the safety impact . 30
7 AI safety management . 31
7.1 Objectives . 31
7.2 Prerequisites and supporting information . 31
7.3 General requirements . 31
7.4 Reference AI safety life cycle . 34
7.5 Iterative development paradigms for AI systems . 1
7.6 Work products . 3
8 Assurance arguments for AI systems . 3
8.1 Objectives . 3
8.2 Prerequisites and supporting information . 3
8.3 General requirements . 4
8.4 AI system-specific considerations in assurance arguments . 4
8.5 Structuring assurance arguments for AI systems . 6
8.5.1 Context of the assurance argument . 6
8.5.2 Categories of evidence . 7
8.6 The role of quantitative targets and qualitative arguments . 8
8.7 Evaluation of the assurance argument . 9
8.8 Work products . 10
9 Derivation of AI safety requirements . 10
9.1 Objectives . 10
9.2 Prerequisites and supporting information . 10
9.3 General requirements . 11
iii
ISO/CD PASDPAS 8800:2024(:(en)
9.4 General workflow for deriving safety requirements . 12
9.5 Deriving AI safety requirements on supervised machine learning . 15
9.5.1 The need for refined AI safety requirements . 15
9.5.2 Derivation of refined AI safety requirements to manage uncertainty . 16
9.5.3 Refinement of the input space definition for AI safety lifecycle . 19
9.5.4 Restricting the occurrence of AI output insufficiencies . 19
9.5.5 Metrics, measurements and threshold design . 23
9.5.6 Considerations for deriving safety requirements . 24
9.6 Work products . 24
10 Selection of AI technologies, architectural and development measures . 25
10.1 Objectives . 25
10.2 Prerequisites . 25
10.3 General requirements . 25
10.4 Architecture and development process design or refinement . 26
10.5 Examples of architectural and development measures for AI systems. 27
10.6 Work products . 30
11 Data-related considerations . 31
11.1 Objectives . 31
11.2 Prerequisites and supporting information . 31
11.3 General requirements . 31
11.4 Dataset life cycle . 32
11.4.1 Datasets and the AI safety lifecycle . 32
11.4.2 Reference dataset lifecycle . 33
11.4.3 Dataset safety analysis . 34
11.4.4 Dataset requirements development . 40
11.4.5 Dataset design . 43
11.4.6 Dataset implementation . 44
11.4.7 Dataset verification . 45
11.4.8 Dataset validation . 46
11.4.9 Dataset maintenance . 46
11.5 Work products . 47
12 Verification and validation of the AI system . 47
12.1 Objectives . 47
12.2 Prerequisites and supporting information . 48
12.3 General requirements . 48
12.4 AI/ML specific challenges to verification and validation . 50
12.5 Verification and validation of the AI system . 51
12.5.1 Scope of verification and validation of the AI system . 51
12.5.2 AI component testing . 1
12.5.3 Methods for testing the AI component . 3
12.5.4 AI system integration and verification. 5
12.5.5 Virtual testing vs physical testing . 6
12.5.6 Evaluation of the safety-related performance of the AI system . 7
12.5.7 AI system safety validation. 8
12.6 Work products .
...
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