Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
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- @Article{pigozzi:2021:AS,
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author = "Federico Pigozzi and Eric Medvet and Laura Nenzi",
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title = "Mining Road Traffic Rules with Signal Temporal Logic
and {Grammar-Based} Genetic Programming",
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journal = "Applied Sciences",
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year = "2021",
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volume = "11",
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number = "22",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/11/22/10573",
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DOI = "doi:10.3390/app112210573",
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abstract = "Traffic systems, where human and autonomous drivers
interact, are a very relevant instance of complex
systems and produce behaviours that can be regarded as
trajectories over time. Their monitoring can be
achieved by means of carefully stated properties
describing the expected behaviour. 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 behaviour 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 behaviour. 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.",
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notes = "also known as \cite{app112210573}",
- }
Genetic Programming entries for
Federico Pigozzi
Eric Medvet
Laura Nenzi
Citations