Navigating The Evolving Landscape Of Safety Standards For Machine Learning Based Road Vehicle Functions
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Navigating the Evolving Landscape of Safety Standards for Machine Learning-based Road Vehicle Functions
ML approaches to solving some of the key perception and decision challenges in automated vehicle functions are maturing at an incredible rate. However, the setbacks experienced during initial attempts at widespread deployment have highlighted the need for a careful consideration of safety during the development and deployment of these functions. To better control the risk associated with this storm of complex functionality, open operating environments, and cutting-edge technology, there is a need for industry consensus on best practices for achieving an acceptable level of safety. Navigating the Evolving Landscape of Safety Standards for Machine Learning-based Road Vehicle Functions provides an overview of standards relevant to the safety of ML-based vehicle functions and serves as guidance for technology providers—including those new to the automotive sector—on how to interpret the evolving standardization landscape. The report also contains practical guidance, along with an example from the perspective of a developer of an ML-based perception function on how to interpret the requirements of these standards. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2024017
SAE J3016 as a Learning Device for the Driving Automation Community
It is the tenth anniversary of SAE International’s Surface Vehicle Recommended Practice effort SAE J3016 to establish a nomenclature standard for driving automation systems and levels of automation. While not exhaustive, this report covers motivation, initiation, and continued development of J3016 regarding driving automation systems, noting that J3016 evolved as a learning device that facilitated the evolution of driving automation systems. It initially worked by establishing common terminology for the technical learning in the field, but over time, J3016 expanded to recognize the human roles in driving automation systems, with later iterations considering broader transportation ecosystems, including fleet operations and remote assistance centers. SAE J3016 as a Learning Device for the Driving Automation Community: Technical, Socio-technical, and Systemic Learning emphasizes ongoing learning to integrate diverse insights about technical, social, and socio-technical challenges of driving automation systems. It also addresses briefly the struggle to accommodate hybrid visions of driving automation systems. It provides a brief history and describes how J3016 changed over time to reflect and support driving automation community learning as lessons for driving automation, whether focused on-road or off-road, concluding with a summary of driving automation challenges and tasks. 9781468608977 9781468608984 https://doi.org/10.4271/EPR2024029
The Challenges of Nextgen ADAS and ADS and Related Vehicle Safety Topics
Advanced driver assistance systems (ADAS) and automated driving systems (ADS) continue to expand into the market at a rapid pace. As improved (i.e., next generation) versions of these systems become available, they will continue to face many challenges in their implementation and benefits for safety and driving operations. The solution will involve many parties, including road safety professionals and researchers who see the potential in these systems but may have difficulties keeping up with them, and safety advocates who are calling for these systems to achieve higher levels of safety now. The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics explores these challenges that will fall on the National Highway Traffic Safety Administration (NHTSA) and automakers as they balance costs and benefits; establish reasonable regulations and standards; and determine how to improve, test, deliver, and use these systems successfully. Perhaps the most formidable challenge will be overcoming the expectation that ADAS—and especially ADS—will always work perfectly in every scenario. ISBN 9781468609301 ISBN 9781468609318 https://doi.org/10.4271/EPR2025003