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Challenges for Automated Cooperative Driving: The AutoNet2030 Approach

  • Marcus Obst
  • Ali Marjovi
  • Milos Vasic
  • Iñaki Navarro
  • Alcherio Martinoli
  • Angelos Amditis
  • Panagiotis Pantazopoulos
  • Ignacio Llatser
  • Arnaud de La Fortelle
  • Xiangjun Qian
Chapter

Abstract

Automated driving is expected to significantly contribute to future safe and efficient mobility. Whereas classical automated approaches solely consider the host vehicle, AutoNet2030 aims to investigate a cooperative approach where communication is used to build decentralized control systems, facilitate cooperative traffic flow optimization, and enhance perception. This chapter introduces the concepts and methodology of AutoNet2030 in order to contribute to a cost-optimized and widely deployable automated driving technology.

Keywords

Automated driving Cooperation Communication V2V 

Notes

Acknowledgement

The research work has been funded by the European FP7 project AutoNet2030 (Grant Agreement NO. 610542).

References

  1. 1.
    A. de La Fortelle, X. Qian, S. Diemer, J. Grégoire, F. Moutarde, S. Bonnabel. A. Marjovi, A. Martinoli, I. Llatser, A. Festag, K. Sjöberg, Network of Automated Vehicles: The AutoNet2030 Vision, in Proceedings of 21st World Congress on Intelligent Transport Systems, 2014Google Scholar
  2. 2.
    L. Xiao, F. Gao, Practical string stability of platoon of adaptive cruise control vehicles. IEEE Trans. Intell. Transp. Syst. 12(4), 1184–1194 (2011)CrossRefGoogle Scholar
  3. 3.
    S.Y. Han, Y.H. Chen, L. Wang, A. Abraham, Decentralized Longitudinal Tracking Control for Cooperative Adaptive Cruise Control Systems in a Platoon, in IEEE International Conference on Systems, Man, and Cybernetics, pp. 2013–2018, 2013Google Scholar
  4. 4.
    K.Y. Liang, Coordination and Routing for Fuel-Efficient Heavy-Duty Vehicle Platoon Formation, Licentiate Thesis, 2014Google Scholar
  5. 5.
    S. Gowal, R. Falconi, A. Martinoli, Local Graph-Based Distributed Control for Safe Highway Platooning, in IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 6070–6076, 2010Google Scholar
  6. 6.
    A. Marjovi, M. Vasic, J. Lemaitre, A. Martinoli, Distributed Graph-Based Convoy Control for Networked Intelligent Vehicles, in Proceedings of IEEE Intelligent Vehicles Symposium, pp. 138–143, 2015Google Scholar
  7. 7.
    K.M. Dresner, P. Stone, A multiagent approach to autonomous intersection management. J. Artif. Intell. Res. (JAIR) 31, 591–656 (2008)CrossRefGoogle Scholar
  8. 8.
    J. Gregoire, S. Bonnabel, A. de La Fortelle, Priority-Based Coordination of Robots, CoRR, vol. abs/1306.0, 2013Google Scholar
  9. 9.
    X. Qian, J. Gregoire, F. Moutarde, A. de La Fortelle, Priority-Based Coordination of Autonomous and Legacy Vehicles at Intersection, in Proceedings of the IEEE International. Conference on Intelligent Transportation Systems (ITSC), pp. 1166–1171, 2014Google Scholar
  10. 10.
    X. Qian, J. Gregoire, A. de La Fortelle, F. Moutarde, Decentralized Model Predictive Control for Smooth Coordination of Automated Vehicles at Intersection, in European Control Conference (ECC2015), 2015Google Scholar
  11. 11.
    N. Mattern, R. Schubert, A Hybrid Approach for ADAS Algorithm Development—From High-Level Prototypes to ECUs, in Proceedings of the 10th ITS European Congress, Helsinki, FinlandGoogle Scholar
  12. 12.
    S. Kato, S. Tsugawa, K. Tokuda, T. Matsui, H. Fujii, Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications. IEEE Trans. Intell. Transp. Syst. 3(3), 155–161 (2002)CrossRefGoogle Scholar
  13. 13.
    H. Stubing, M. Bechler, D. Heussner, T. May, I. Radusch, H. Rechner, P. Vogel, simTD: A car-to-X system architecture for field operational tests. IEEE Commun. Mag. 48(5), 148–154 (2010)CrossRefGoogle Scholar
  14. 14.
    L. Hobert, A. Festag, I. Llatser, L. Altomare, F. Visintainer, A. Kovacs, Enhancements of V2X Communication in Support of Cooperative Autonomous Driving, to appear in IEEE Communications Magazine, 2015Google Scholar
  15. 15.
    M. Tsogas, N. Floudas, P. Lytrivis, A. Amditis, A. Polychronopoulos, Combined lane and road attributes extraction by fusing data from digital map, laser scanner and camera. Inf. Fusion 12(1), 28–36 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Marcus Obst
    • 1
  • Ali Marjovi
    • 2
  • Milos Vasic
    • 2
  • Iñaki Navarro
    • 2
  • Alcherio Martinoli
    • 2
  • Angelos Amditis
    • 3
  • Panagiotis Pantazopoulos
    • 3
  • Ignacio Llatser
    • 4
  • Arnaud de La Fortelle
    • 5
  • Xiangjun Qian
    • 5
  1. 1.BaselabsChemnitzGermany
  2. 2.École Polytechnique Fédérale de LausanneLausanneSwitzerland
  3. 3.Institute of Communications and Computer SystemsAthensGreece
  4. 4.Technische Universität DresdenDresdenGermany
  5. 5.Centre for RoboticsMINES ParisTech—PSL Research UniversityParisFrance

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