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Article

Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

1
Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy
2
Department of Mathematics and Geosciences, University of Trieste, 34127 Trieste, Italy
3
TU Wien Informatics, 1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Paweł Droździel, Radovan Madleňák, Saugirdas Pukalskas, Drago Sever and Marcin Ślęzak
Appl. Sci. 2021, 11(22), 10573; https://doi.org/10.3390/app112210573
Received: 8 October 2021 / Revised: 29 October 2021 / Accepted: 4 November 2021 / Published: 10 November 2021
Traffic systems, where human and autonomous drivers interact, are a very relevant instance of complex systems and produce behaviors that can be regarded as trajectories over time. Their monitoring can be achieved by means of carefully stated properties describing the expected behavior. Such properties can be expressed using Signal Temporal Logic (STL), a specification language for expressing temporal properties in a formal and human-readable way. However, manually authoring these properties is a hard task, since it requires mastering the language and knowing the system to be monitored. Moreover, in practical cases, the expected behavior is not known, but it has instead to be inferred from a set of trajectories obtained by observing the system. Often, those trajectories come devoid of human-assigned labels that can be used as an indication of compliance with expected behavior. As an alternative to manual authoring, automatic mining of STL specifications from unlabeled trajectories would enable the monitoring of autonomous agents without sacrificing human-readability. In this work, we propose a grammar-based evolutionary computation approach for mining the structure and the parameters of an STL specification from a set of unlabeled trajectories. We experimentally assess our approach on a real-world road traffic dataset consisting of thousands of vehicle trajectories. We show that our approach is effective at mining STL specifications that model the system at hand and are interpretable for humans. To the best of our knowledge, this is the first such study on a set of unlabeled real-world road traffic data. Being able to mine interpretable specifications from this kind of data may improve traffic safety, because mined specifications may be helpful for monitoring traffic and planning safety promotion strategies. View Full-Text
Keywords: context-free grammar genetic programming; grammatical evolution; traffic monitoring; formal methods context-free grammar genetic programming; grammatical evolution; traffic monitoring; formal methods
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MDPI and ACS Style

Pigozzi, F.; Medvet, E.; Nenzi, L. Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming. Appl. Sci. 2021, 11, 10573. https://doi.org/10.3390/app112210573

AMA Style

Pigozzi F, Medvet E, Nenzi L. Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming. Applied Sciences. 2021; 11(22):10573. https://doi.org/10.3390/app112210573

Chicago/Turabian Style

Pigozzi, Federico, Eric Medvet, and Laura Nenzi. 2021. "Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming" Applied Sciences 11, no. 22: 10573. https://doi.org/10.3390/app112210573

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