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Priority-Based Traffic Management Protocols for Autonomous Vehicles on Road Networks

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AI 2021: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13151))

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Abstract

This paper describes a generic simulation platform for testing traffic management protocols on road networks with autonomous vehicles. Firstly, we introduce a formal model to represent a road network as a directed multigraph. We then describe traffic management protocols in terms of the priority over roads or vehicles. Based the model, we developed a system that can simulate complex road networks with traffic of autonomous vehicles under the management of different traffic control protocols in different intersections. The system was build up on the existing platform AIM4. With the simulation system, we can test a variety of properties of traffic management protocols from macro and micro perspectives of traffic network with autonomous vehicles.

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Notes

  1. 1.

    https://www.cs.utexas.edu/~aim/.

  2. 2.

    Although the system can take any input of a road graph and a configuration of priorities, the capacity of roads and vehicles are limited by computer hardware and GUI setting.

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Author information

Authors and Affiliations

  1. Western Sydney University, Penrith, Australia

    Jianglin Qiao & Dongmo Zhang

  2. Artificial Intelligence Research Institute (IIIA-CSIC), Barcelona, Spain

    Jianglin Qiao & Dave de Jonge

Authors
  1. Jianglin Qiao
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  2. Dongmo Zhang
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  3. Dave de Jonge
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Corresponding author

Correspondence to Jianglin Qiao .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  2. RMIT University, Melbourne, SA, Australia

    Xinghuo Yu

  3. University of Queensland, Brisbane, QLD, Australia

    Sen Wang

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Qiao, J., Zhang, D., de Jonge, D. (2022). Priority-Based Traffic Management Protocols for Autonomous Vehicles on Road Networks. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-97546-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97545-6

  • Online ISBN: 978-3-030-97546-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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