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

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 properti...

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Autores principales:	Federico Pigozzi, Eric Medvet, Laura Nenzi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	context-free grammar genetic programming grammatical evolution traffic monitoring formal methods Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/62a80a53101049e0b56cae4509bdf4ab
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spelling	oai:doaj.org-article:62a80a53101049e0b56cae4509bdf4ab2021-11-25T16:31:54ZMining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming10.3390/app1122105732076-3417https://doaj.org/article/62a80a53101049e0b56cae4509bdf4ab2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10573https://doaj.org/toc/2076-3417Traffic 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.Federico PigozziEric MedvetLaura NenziMDPI AGarticlecontext-free grammar genetic programminggrammatical evolutiontraffic monitoringformal methodsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10573, p 10573 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	context-free grammar genetic programming grammatical evolution traffic monitoring formal methods Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	context-free grammar genetic programming grammatical evolution traffic monitoring formal methods Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Federico Pigozzi Eric Medvet Laura Nenzi Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
description	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.
format	article
author	Federico Pigozzi Eric Medvet Laura Nenzi
author_facet	Federico Pigozzi Eric Medvet Laura Nenzi
author_sort	Federico Pigozzi
title	Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
title_short	Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
title_full	Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
title_fullStr	Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
title_full_unstemmed	Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
title_sort	mining road traffic rules with signal temporal logic and grammar-based genetic programming
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/62a80a53101049e0b56cae4509bdf4ab
work_keys_str_mv	AT federicopigozzi miningroadtrafficruleswithsignaltemporallogicandgrammarbasedgeneticprogramming AT ericmedvet miningroadtrafficruleswithsignaltemporallogicandgrammarbasedgeneticprogramming AT lauranenzi miningroadtrafficruleswithsignaltemporallogicandgrammarbasedgeneticprogramming
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Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming

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