Sky Bear Technical Standards - Home
Search
×
FR

Placeholder headline

This is just a placeholder headline

TEMA 2026 Edition – Online Subscription Only

$

BUY NOW

Placeholder headline

This is just a placeholder headline

API RP 15SIH, 2nd Edition: Installation and Handling of Spoolable Reinforced Plastic Line Pipe

$

158

BUY NOW

Placeholder headline

This is just a placeholder headline

API RP 754, 4th Edition: Process Safety Performance Indicators for the Refining and Petrochemical Industries

$

393

BUY NOW

Placeholder headline

This is just a placeholder headline

ASME B31.1-2020: Power Piping

$

668

BUY NOW

Placeholder headline

This is just a placeholder headline

ASME B31.1-2022: Power Piping

$

668

BUY NOW

ISO 8800:2024

ISO 8800:2024 Road vehicles – Safety and artificial intelligence

CDN $438.00

Description

This document applies to safety-related systems that include one or more electrical and/or electronic (E/E) systems that use AI technology and that is installed in series production road vehicles, excluding mopeds. It does not address unique E/E systems in special vehicles, such as E/E systems designed for drivers with disabilities.

This document addresses the risk of undesired safety-related behaviour at the vehicle level due to output insufficiencies, systematic errors and random hardware errors of AI elements within the vehicle. This includes interactions with AI elements that are not part of the vehicle itself but that can have a direct or indirect impact on vehicle safety.

EXAMPLE 1         Examples of AI elements within the vehicle include the trained AI model and AI system.

EXAMPLE 2         Direct impact on safety can be due to object detection by elements external to the vehicle.

EXAMPLE 3         Indirect impact on safety can be due to field monitoring by elements external to the vehicle.

The development of AI elements that are not part of the vehicle is not within the scope of this document. These elements can conform to domain-specific safety guidance. This document can be used as a reference where such domain-specific guidance does not exist.

This document describes safety-related properties of AI systems that can be used to construct a convincing safety assurance claim for the absence of unreasonable risk.

This document does not provide specific guidelines for software tools that use AI methods.

This document focuses primarily on a subclass of AI methods defined as machine learning (ML). Although it covers the principles of established and well-understood classes of ML, it does not focus on the details of any specific AI methods e.g. deep neural networks.

Edition

1

Published Date

2024-12-13

Status

PUBLISHED

Pages

172

Language Detail Icon

English

Format Secure Icon

Secure PDF

Abstract

This document applies to safety-related systems that include one or more electrical and/or electronic (E/E) systems that use AI technology and that is installed in series production road vehicles, excluding mopeds. It does not address unique E/E systems in special vehicles, such as E/E systems designed for drivers with disabilities.

This document addresses the risk of undesired safety-related behaviour at the vehicle level due to output insufficiencies, systematic errors and random hardware errors of AI elements within the vehicle. This includes interactions with AI elements that are not part of the vehicle itself but that can have a direct or indirect impact on vehicle safety.

EXAMPLE 1         Examples of AI elements within the vehicle include the trained AI model and AI system.

EXAMPLE 2         Direct impact on safety can be due to object detection by elements external to the vehicle.

EXAMPLE 3         Indirect impact on safety can be due to field monitoring by elements external to the vehicle.

The development of AI elements that are not part of the vehicle is not within the scope of this document. These elements can conform to domain-specific safety guidance. This document can be used as a reference where such domain-specific guidance does not exist.

This document describes safety-related properties of AI systems that can be used to construct a convincing safety assurance claim for the absence of unreasonable risk.

This document does not provide specific guidelines for software tools that use AI methods.

This document focuses primarily on a subclass of AI methods defined as machine learning (ML). Although it covers the principles of established and well-understood classes of ML, it does not focus on the details of any specific AI methods e.g. deep neural networks.

Previous Editions

Can’t find what you are looking for?

Please contact us at: