Divya Garikapati

and 2 more

The traditional approach to applying safety limits in electro-mechanical systems across various industries, including automated vehicles, robotics, and aerospace, involves hard-coding control and safety limits into production firmware, which remains fixed throughout the product life cycle. However, with the evolving needs of automated systems like automated vehicles and robots, this approach falls short in addressing all use cases and scenarios to ensure safe operation. Particularly for data-driven machine learning applications that continuously evolve, there’s a need for a more flexible and adaptable safety limits application strategy based on different Operational Design Domains (ODDs) and scenarios. Our ITSC conference paper [(1)] introduced the Dynamic Control Limits Application (DCLA) strategy, supporting the flexible application of diverse limits profiles based on dynamic scenario parameters across different layers of the Autonomy software stack. This paper extends the DCLA strategy by outlining a methodology for safety limits application based on ODD elements, scenario identification, and classification using Decision Making Engines. It also utilizes a layered architecture and cloud infrastructure based on Vehicle-to-Infrastructure (V2I) technology to store scenarios and limits mapping as a ground truth or backup mechanism for the Decision Making Engine. Additionally, the paper focuses on providing a comprehensive list of scenarios and an experimental dataset covering maximum ODD elements, along with multiple tables of safety limits to create various application profiles. These profiles, based on perceived scenario parameters, can be systematically applied or trained on Decision Making algorithms, offering a scalable solution for future automated vehicles and systems up to Level 5 Autonomy within the industry.