A Unified Self-Assessment Framework for Autonomous Driving Stacks Using Subjective Logic

Abstract

Self-assessment plays a critical and important role toward safe and robust autonomous driving. Current self-assessment approaches in this area focus on individual modules at specific positions within the autonomous driving stack. The literature lacks a unifying framework to combine various self-assessment information. Hence, this work provides a comprehensive self-assessment framework for autonomous driving stacks, combining and unifying existing self-assessment methods. For this framework, we propose using subjective logic as an interface to standardize the output of self-assessment modules. This allows the combination of different modules and their use in subsequent processing modules. Our approach can be deployed to existing autonomous vehicle software stacks without imposing any requirements on their functional parts, enabling easy integration. With this framework, we are aiming to contribute to the improvement of safety and reliability in autonomous driving.