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 elements, scenario identification, and
classification using decision-making (DM) 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 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.
