Keysight Collaborates with the Centre for Assuring Autonomy at the University of York to Advance AI Safety Assurance for Software-Defined Vehicles

Keysight and the University of York will co-develop evidence-driven AI safety methodologies for software-defined vehicles, aligned with emerging automotive standards.

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Research initiative aims to close gap between AI safety research and practical engineering to reduce risk and enable faster, safer deployment of AI-enabled automotive systems

SANTA ROSA, Calif.--(BUSINESS WIRE)-- Keysight Technologies, Inc. (NYSE: KEYS) and the Centre for Assuring Autonomy (CfAA) at the University of York (UK) announced a research collaboration to advance the safe deployment of artificial intelligence (AI) in software-defined vehicles (SDVs). The collaboration will focus on developing practical methods for validating AI systems and generating the evidence needed to demonstrate their safety and reliability. The work is designed to enable automakers and suppliers to meet emerging industry requirements while reducing development risk and accelerating the deployment of AI-enabled automotive systems.

Artificial intelligence is rapidly becoming a core technology in advanced driver assistance systems (ADAS), automated driving functions, and SDVs. As these capabilities become more sophisticated, automotive organizations face growing pressure to demonstrate that AI systems operate safely, reliably, and as intended, not only during development but throughout the vehicle lifecycle. Providing that proof is one of the industry’s most significant challenges as automakers work to bring increasingly intelligent vehicles to market while meeting evolving regulatory and safety expectations.

Together, Keysight and the CfAA will help bridge the gap between AI safety research and practical engineering by advancing methodologies for structured AI safety cases and building justified confidence in the safety of AI-enabled automotive systems.

The research will focus on the creation of evidence-driven approaches to support the implementation of ISO/PAS 8800 requirements, exploring measurable safety-scoring methodologies grounded in academic research and industry standards, and creating practical frameworks for generating auditable AI safety evidence. The resulting methodologies are intended to support the process of building AI safety cases, improve confidence in AI validation, and help engineering teams demonstrate justified AI safety more efficiently throughout the product lifecycle.

The University of York is internationally recognized for its research in safety engineering, and the assurance of complex systems across domains, including transport and automotive. By combining the university’s academic expertise with Keysight's AI validation techniques, the collaboration seeks to advance methodologies applicable to automotive AI development.

Simon Burton, Chair in Systems Safety, University of York, said: “The automotive industry is at a pivotal point where AI technologies are becoming increasingly integral to vehicle functionality. Ensuring these systems can be evaluated using robust, evidence-based approaches is essential. This is where the CfAA is ideally placed to support Keysight. We have produced several freely accessible frameworks and guidance already being used by industry safety professionals in the transport sector. This collaboration is another way we are supporting the advancement of practice methods that help translate AI safety principles into engineering practices that can be applied in safety-critical environments.”

Lukas Klose, Head of the Automotive AI Solution Center, Keysight, said: “Automotive organizations need practical and scalable ways to build confidence in AI-enabled systems. By combining leading research in safety assurance with Keysight's holistic AI Validation Framework, we aim to develop methodologies that help engineering teams generate structured evidence for AI safety cases and support the deployment of trustworthy AI technologies in conformance with international standards such as ISO/PAS 8800.”

The research is expected to inform future development of Keysight's AI Software Integrity Builder, enable enhanced support for AI safety arguments, safety evidence generation, and validation activities aligned with automotive industry expectations.

Resources

• Web Page: AX1000A Keysight AI Software Integrity Builder
• Flyer: Can You Explain Your AI and Prove AI Safety in Deployment?
• Video: Professor Simon Burton, CfAA Chair of Systems Safety, introduces ISO PAS 8800 for automated driving

About Keysight Technologies

At Keysight (NYSE: KEYS), we inspire and empower innovators to bring world-changing technologies to life. As an S&P 500 company, we’re delivering market-leading design, emulation, and test solutions to help engineers develop and deploy faster, with less risk, throughout the entire product life cycle. We’re a global innovation partner enabling customers in communications, industrial automation, aerospace and defense, automotive, semiconductor, and general electronics markets to accelerate innovation to connect and secure the world. Learn more at Keysight Newsroom and www.keysight.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20260903072390/en/

Keysight Media Contacts

Andrea Mueller
Americas
Andrea.mueller@keysight.com

Fusako Dohi
Asia
fusako_dohi@keysight.com

Jenny Gallacher
Europe
Jenny.gallacher@keysight.com

Source: Keysight Technologies, Inc.

Key Terms

adas technical

Advanced Driver Assistance Systems (ADAS) are electronic systems in vehicles that assist the driver with safety tasks. Examples include automatic emergency braking, lane keeping assist, and adaptive cruise control. These systems use sensors and cameras to improve vehicle safety.

software-defined vehicles technical

Vehicles whose key features and performance are controlled and updated primarily through software rather than fixed hardware designs. Like a smartphone that gains new apps and capabilities over time, these cars can receive over-the-air updates that add features, improve efficiency, or fix issues, which matters to investors because it can extend product life, create ongoing revenue from software services, lower recall risk, and change how value is created and captured in the auto industry.