Abstract
Correctness of autonomous driving systems is crucial as incorrect behaviour may have catastrophic consequences. Many different hardware and software components (e.g. sensing, decision making, actuation, and control) interact to solve the autonomous driving task, leading to a level of complexity that brings new challenges for the formal verification community. Though formal verification has been used to prove correctness of software, there are significant challenges in transferring such techniques to an agile software development process and to ensure widespread industrial adoption. In the light of these challenges, the identification of appropriate formalisms, and consequently the right verification tools, has significant impact on addressing them. In this paper, we evaluate the application of different formal techniques from supervisory control theory, model checking, and deductive verification to verify existing decision and control software (in development) for an autonomous vehicle. We discuss how the verification objective differs with respect to the choice of formalism and the level of formality that can be applied. Insights from the case study show a need for multiple formal methods to prove correctness, the difficulty to capture the right level of abstraction to model and specify the formal properties for the verification objectives.
Supported by FFI, VINNOVA under grant number 2017-05519, Automatically Assessing Correctness of Autonomous Vehicles–Auto-CAV.
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Notes
- 1.
In the SCT framework, the specification is the property of interest to verify with respect to the plant.
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Selvaraj, Y., Ahrendt, W., Fabian, M. (2019). Verification of Decision Making Software in an Autonomous Vehicle: An Industrial Case Study. In: Larsen, K., Willemse, T. (eds) Formal Methods for Industrial Critical Systems. FMICS 2019. Lecture Notes in Computer Science(), vol 11687. Springer, Cham. https://doi.org/10.1007/978-3-030-27008-7_9
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