Abstract

This paper deals with optimization strategies in cooperative System-of-Systems. Intelligent systems which want to collaborate need to align their behaviour with respect to individual and group goals. We derive a methodology to generate optimization functions for group members and to normalize them across the fleet. We consider the example of autonomous cars driving in a platoon. For this example we show to derive optimal vehicle speed and distances based on individual preferences. We describe a prototypical implementation of our approach and give some experimental results.

This research was funded by the Federal Ministry of Education and Research (BMBF-WIR!) within the PEELIKAN-project and by the Berlin Institute for Applied Research (IFAF Berlin) within the CARS-project.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Brazil)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (Brazil)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (Brazil)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ensemble: ENSEMBLE project: the new frontier of multi-brand truck platooning — Platooning Ensemble (2023). https://platooningensemble.eu/news/ensembleprojectthenewfrontierofmultibrandtruckplatooning61a4eec1f0507. Accessed 11 Apr 2023

  2. Faraj, M., Sancar, F.E., Fidan, B.: Platoon-based autonomous vehicle speed optimization near signalized intersections. In: 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1299–1304 (2017). https://doi.org/10.1109/IVS.2017.7995891

  3. Gan, E., Fong, M., Ng, Y.L.: CFD Analysis of slipstreaming and side drafting techniques concerning aerodynamic drag in NASCAR racing. CFD Letters 12, 116 (2020). https://doi.org/10.37934/cfdl.12.7.116

  4. Gür, Ş, Eren, T.: Scheduling and planning in service systems with goal programming: literature review. Mathematics 6(11), 265 (2018). https://doi.org/10.3390/math6110265

    Article  Google Scholar 

  5. Hu, M., Zhao, X., Hui, F., Tian, B., Xu, Z., Zhang, X.: Modeling and analysis on minimum safe distance for platooning vehicles based on field test of communication delay. J. Adv. Transp. 2021, 1–15 (2021). https://doi.org/10.1155/2021/5543114

  6. Kühlwein, J.: Driving resistances of light-duty vehicles in Europe: present situation, trends, and scenarios for 2025. https://theicct.org/sites/default/files/publications/ICCT_LDV-Driving-Resistances-EU_121516.pdf. Accessed 21 Apr 2023

  7. Kumari, J.R.: Optimization techniques - a review. Int. J. Eng. Res. Appl. 6(11), 01–05 (2016). https://www.ijera.com/papers/Vol6_issue11/Part-4/A0611040105.pdf. Accessed 21 Apr 2023

  8. Löhe, U.: Geschwindigkeiten auf der Bundesautobahn in den Jahren 2010 bis 2014. Tech. rep., Bundesanstalt für Straßenwesen (2016). https://opus4.hbz-nrw.de/opus45-bast/frontdoor/deliver/index/docId/2612/file/Geschwindigkeiten-BAB-2010-2014.pdf.pdf. Accessed 12 Apr 2023

  9. Scholz, D.: Multikriterielle optimierung. In: Optimierung interaktiv, pp. 169–187. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-57953-4_7

    Chapter  Google Scholar 

  10. Sinrich, D.: An architectural blueprint for autonomic computing (2005). https://www-03.ibm.com/autonomic/pdfs/AC%20Blueprint%20White%20Paper%20V7.pdf. Accessed 15 Apr 2023

  11. Sturm, T., Krupitzer, C., Segata, M., Becker, C.: A taxonomy of optimization factors for platooning. IEEE Trans. Intell. Transp. Syst. 22(10), 6097–6114 (2021). https://doi.org/10.1109/TITS.2020.2994537

    Article  Google Scholar 

  12. Wang, F., Zhuang, W., Yin, G., Liu, S., Liu, Y., Dong, H.: Robust inter-vehicle distance measurement using cooperative vehicle localization. Sensors 21(6), 2048 (2021). https://doi.org/10.3390/s21062048

    Article  Google Scholar 

  13. Wasserburger, A., Schirrer, A., Hametner, C.: Stochastic optimization for energy-efficient cooperative platooning. In: 2019 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1–6 (2019). https://doi.org/10.1109/VPPC46532.2019.8952431

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Björn Wudka .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wudka, B., Thomas, C., Schlingloff, BH. (2023). A Cooperative Decentralised Optimization Method for Vehicle Platooning. In: Jove, E., Zayas-Gato, F., Michelena, Á., Calvo-Rolle, J.L. (eds) Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-031-38616-9_8

Download citation

Publish with us

Policies and ethics

Navigation