Verification of Decision Making Software in an Autonomous Vehicle: An Industrial Case Study

Abstract

Correctness of autonomous driving systems is crucial as\ua0incorrect behaviour may have catastrophic consequences. Many different\ua0hardware and software components (e.g. sensing, decision making, actuation,\ua0and control) interact to solve the autonomous driving task, leading to a level of complexity that brings new challenges for the formal verification\ua0community. Though formal verification has been used to prove\ua0correctness of software, there are significant challenges in transferring\ua0such techniques to an agile software development process and to ensure\ua0widespread industrial adoption. In the light of these challenges, the identification\ua0of appropriate formalisms, and consequently the right verification\ua0tools, has significant impact on addressing them. In this paper, we\ua0evaluate the application of different formal techniques from supervisory\ua0control theory, model checking, and deductive verification to verify existing\ua0decision and control software (in development) for an autonomous\ua0vehicle. We discuss how the verification objective differs with respect tothe choice of formalism and the level of formality that can be applied.\ua0Insights from the case study show a need for multiple formal methods to\ua0prove correctness, the difficulty to capture the right level of abstraction\ua0to model and specify the formal properties for the verification objectives