Cited By
View all- Selvaraj YFarooqui APanahandeh GAhrendt WFabian M(2022)Automatically Learning Formal Models from Autonomous Driving SoftwareElectronics10.3390/electronics1104064311:4(643)Online publication date: 18-Feb-2022
Automatically learning formal models: an industrial case from autonomous driving development
Article No.: 33, Pages 1 - 10
Abstract
The correctness of autonomous driving software is of utmost importance as incorrect behaviour may have catastrophic consequences. Though formal model-based engineering techniques can help guarantee correctness, challenges exist in widespread industrial adoption. One among them is the model construction problem. Manual construction of formal models is expensive, error-prone, and intractable for large systems. Automating model construction would be a great enabler for the use of formal methods to guarantee software correctness and thereby for safe deployment of autonomous vehicles. Such automated techniques can be beneficial in software design, re-engineering, and reverse engineering. In this industrial case study, we apply active learning techniques to obtain formal models from an existing autonomous driving software (in development) implemented in MATLAB. We demonstrate the feasibility of active automata learning algorithms for automotive industrial use. Furthermore, we discuss the practical challenges in applying automata learning and possible directions for integrating automata learning into automotive software development workflow.
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Published: 26 October 2020
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View all- Selvaraj YFarooqui APanahandeh GAhrendt WFabian M(2022)Automatically Learning Formal Models from Autonomous Driving SoftwareElectronics10.3390/electronics1104064311:4(643)Online publication date: 18-Feb-2022
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