Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception

Overview

The paper evaluates four architectural patterns for responding to uncertainty in data-driven perception. A vehicle-platooning distance controller illustrates how driving context and inherent risk can adjust tolerated uncertainty while balancing safety and performance.