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Road Traffic Rules Synthesis Using Grammatical Evolution

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Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10200))

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Abstract

We consider the problem of the automatic synthesis of road traffic rules, motivated by a future scenario in which human and machine-based drivers will coexist on the roads: in that scenario, current road rules may be either unsuitable or inefficient. We approach the problem using Grammatical Evolution (GE). To this end, we propose a road traffic model which includes concepts amenable to be regulated (e.g., lanes, intersections) and which allows drivers to temporarily evade traffic rules when there are no better alternatives. In our GE framework, each individual is a set of rules and its fitness is a weighted sum of traffic efficiency and safety, as resulting from a number of simulations where all drivers are subjected to the same rules. Experimental results show that our approach indeed generates rules leading to a safer and more efficient traffic than enforcing no rules or rules similar to those currently used.

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Acknowledgements

The authors are grateful to Lorenzo Castelli for his insightful comments.

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Authors and Affiliations

  1. Department of Engineering and Architecture, University of Trieste, Trieste, Italy

    Eric Medvet, Alberto Bartoli & Jacopo Talamini

Authors
  1. Eric Medvet
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  2. Alberto Bartoli
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  3. Jacopo Talamini
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Corresponding author

Correspondence to Eric Medvet .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Medvet, E., Bartoli, A., Talamini, J. (2017). Road Traffic Rules Synthesis Using Grammatical Evolution. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10200. Springer, Cham. https://doi.org/10.1007/978-3-319-55792-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-55792-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55791-5

  • Online ISBN: 978-3-319-55792-2

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