Abstract
Identifying traffic platoons and managing vehicles on the road effectively is a challenging task that is currently under investigation both in academia and the industry. The challenges include the need for fast, real-time gathering of relevant information, such as the vehicle’s location, moving direction, and their speed, and then instructing the vehicles in real time to respect the traffic policies according to the gathered information. In this work, we present new algorithms to define platoons that are updated dynamically on the fly, allowing much better control over the traffic, thus improving efficiency. The platoon’s representative vehicle is chosen and the set of vehicles in the platoon is identified based on inductive distance criteria, which are continuously checked, and by considering the road graph topology as well as updating the platoon memberships. In the first part of this paper, we present the main algorithms to identify and control the platoon and demonstrate this detection using a vehicle simulator. We show various policies to control the platoon movement, when detecting obstacles, or other unusual situations. In the second part, we explain how to make vehicles travel roughly in the same direction as the platoon, and join the platoon at particular points or junctions. The described algorithms are demonstrated by extensive simulations showing the platoon behavior over time.
Similar content being viewed by others
Data Availability
The code that the simulations based on, can be found in here [23].
References
Zhao W, Ngoduy D, Shepherd S, Liu R, Papageorgiou M. A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. Transp Res Part C Emerg Technol. 2018;95:802–21.
Lopez PA, Behrisch M, Bieker-Walz L, Erdmann J, Flötteröd Y-P, Hilbrich R, Lücken L, Rummel J, Wagner P, Wießner E. Microscopic traffic simulation using sumo. In: IEEE Intelligent Transportation Systems Conference (ITSC) (2018). https://elib.dlr.de/124092/.
Bergenhem C, Pettersson H, Coelingh E, Englund C, Shladover S, Tsugawa S, Adolfsson M. Overview of platooning systems. In: Proceedings of the 19th ITS World Congress. 2012. p. 1–7. http://publications.lib.chalmers.se/publication/174621.
Bergenhem C, Hedin E, Skarin D. Vehicle-to-vehicle communication for a platooning system. Procedia Soc Behav Sci. 2012;48:1222–33.
Michael JB, Godbole DN, Lygeros J, Sengupta R. Capacity analysis of traffic flow over a single-lane automated highway system. Intell Transp Syst J (ITS). 1998;4(1–2):49–80. https://doi.org/10.1080/10248079808903736.
Rajamani R, Tan H-S, Law BK, Zhang W-B. Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons. IEEE Trans Control Syst Technol. 2000;8(4):695–708.
Lauer M. Grand cooperative driving challenge 2011 [its events]. IEEE Intell Transp Syst Mag. 2011;3:38–40.
Tsugawa S, Kato S, Aoki K. An automated truck platoon for energy saving. In: 2011 IEEE/RSJ international conference on intelligent robots and systems. 2011. p. 4109–4114.
Alam A. Fuel-efficient distributed control for heavy duty vehicle platooning. KTH Royal Institute of Technology. QC 20111012. 2011.
Rajamani R, Tan H, Law BK, Zhang W. Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons. IEEE Trans Control Syst Technol. 2000;8(4):695–708. https://doi.org/10.1109/87.852914.
Browand F, McArthur J, Radovich C. Fuel saving achieved in the field test of two tandem trucks. Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt29v570mm, Institute of Transportation Studies, UC Berkeley (2004). https://ideas.repec.org/p/cdl/itsrrp/qt29v570mm.html.
Tsugawa S, Kato S, Aoki K. An automated truck platoon for energy saving. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 2011, 25–30. IEEE; 2011. p. 4109–4114. https://doi.org/10.1109/IROS.2011.6094549.
He Q, Head KL, Ding J. Pamscod: platoon-based arterial multi-modal signal control with online data. Transp Res Part C Emerg Technol. 2012;20(1):164–84.
Tiaprasert K, Zhang Y, Ye X. Platoon recognition using connected vehicle technology. J Intell Transp Syst. 2019;23(1):12–27. https://doi.org/10.1080/15472450.2018.1476146.
Patrizi N, Fragkos G, Ortiz K, Oishi M, Tsiropoulou EE. A UAV-enabled dynamic multi-target tracking and sensing framework. In: GLOBECOM 2020—2020 IEEE Global Communications Conference. 2020. p. 1–6.
Irigon de Irigon J, Walter F, Springer T. Evaluation of DTN routing algorithms in scheduled public transport networks. In: Grieco LA, Boggia G, Piro G, Jararweh Y, Campolo C, editors. Ad-hoc, mobile, and wireless networks. Cham: Springer; 2020. p. 37–52.
Barbeau M, Garcia-Alfaro J, Kranakis E. Geocaching-inspired navigation for micro aerial vehicles with fallible place recognition. In: Ad-hoc, mobile, and wireless networks: 19th international conference on ad-hoc networks and wireless, ADHOC-NOW 2020, Bari, Italy, October 19–21, 2020, Proceedings 19. Springer; 2020. p. 55–70.
Chen Z, Park BB. Preceding vehicle identification for cooperative adaptive cruise control platoon forming. IEEE Trans Intell Transp Syst. 2020;21(1):308–20. https://doi.org/10.1109/TITS.2019.2891353.
Praveen PS, Ashalatha R. Identification of platoon dispersion pattern under heterogeneous traffic conditions. Case Stud Transp Policy. 2020;8(1):101–11.
Fricke LB. Northwestern University (Evanston, I.T.I.: Traffic accident reconstruction, 1990). https://books.google.co.il/books?id=KPoxpwAACAAJ.
Ericsson. Remote operation of vehicles with 5G (2023). https://www.ericsson.com/en/reports-and-papers/mobility-report/articles/remote-monitoring-and-control-of-vehicles.
Hannah Yair EG, Shlomi D. Demonstration videos (2022). https://drive.google.com/drive/folders/1JMhSIa7injfr70emgL4PXzJVm GITnfU6?usp=sharing.
Funding
This study was funded by the Andromeda MAGNET Consortium of the Israeli Innovation Authority (Grant Number 77841) and partially by the The Israeli Smart Transportation Research Center (ISTRC).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yair, H., Dolev, S. & Gudes, E. Automatic Real-Time Platoon Formation Using the Road Graph. SN COMPUT. SCI. 5, 18 (2024). https://doi.org/10.1007/s42979-023-02322-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02322-x