Systematic Selective Limits Application Using Decision-Making Engines to Enhance Safety in Highly Automated Vehicles

The traditional approach to applying safety limits in electromechanical 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 such as 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 is a need for a more flexible and adaptable safety limits application strategy based on different operational design domains (ODDs) and scenarios. The 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 article extends the DCLA strategy by outlining a methodology for safety limits application based on ODD 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 DM engine. Additionally, the article focuses on providing a subset of driving scenarios as case studies that correspond to a subset of the ODD elements, which forms the baseline to derive the safety limits and create four different application profiles or classes of limits. Finally, the real-world examples of "driving-in-rain" scenario variations have been considered to apply DM engines and classify them into the previously identified limits application profiles or classes. This example can be further compared with different DM engines as a future work potential that offers a scalable solution for automated vehicles and systems up to Level 5 Autonomy within the industry.