Advertisement

The Role of Multisensor Environmental Perception for Automated Driving

  • Robin Schubert
  • Marcus Obst
Chapter

Abstract

In order to facilitate automated driving, a reliable representation of a vehicle’s environment is required. This chapter provides a survey of techniques for the perception of both static and dynamic environments including key algorithms for object tracking and data fusion. In addition, the particular challenges of this field from a practitioner’s perspective are discussed and compared to the state-of-the-art design and implementation paradigms.

Keywords

Perception Data fusion Object tracking Occupancy grids 

References

  1. 1.
    B. Khaleghi et al., Multisensor data fusion: A review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)CrossRefGoogle Scholar
  2. 2.
    R. Schubert et al., Empirical evaluation of vehicular models for ego motion estimation, in Intelligent Vehicles Symposium (IV), 2011 IEEE. IEEE (2011)Google Scholar
  3. 3.
    S. Thrun et al., Probabilistic Robotics (The MIT Press, Cambridge, 2005)zbMATHGoogle Scholar
  4. 4.
    P.C. Mahalanobis, On the generalised distance in statistics. Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936)MathSciNetzbMATHGoogle Scholar
  5. 5.
    D. Musicki, R. Evans, Joint Integrated Probabilistic Data Association—JIPDA, in Information Fusion, 2002. Proceedings of the Fifth International Conference on, vol. 2, pp. 1120–1125, 8–11 Jul 2002Google Scholar
  6. 6.
    C. Adam, R. Schubert, G. Wanielik, Radar-based extended object tracking under clutter using generalized probabilistic data association, in Intelligent Transportation Systems—(ITSC), 2013 16th International IEEE Conference on, pp. 1408–1415, 6–9 Oct 2013. doi:  10.1109/ICIF.2002.1020938
  7. 7.
    R. Danescu, F. Oniga, S. Nedevschi, Modeling and tracking the driving environment with a particle-based occupancy grid. Intell. Transp. Syst. IEEE Trans. 12(4), 1331–1342 (2011). doi: 10.1109/TITS.2011.2158097 CrossRefGoogle Scholar
  8. 8.
    C. Coué et al., Bayesian occupancy filtering for multitarget tracking: An automotive application. Int. J. Robot. Res. 25(1), 19–30 (2006)CrossRefGoogle Scholar
  9. 9.
    M.M. Muntzinger et al., Reliable automotive pre-crash system with out-of-sequence measurement processing, in Intelligent Vehicles Symposium (IV), 2010 IEEE. IEEE (2010)Google Scholar
  10. 10.
    A. Rauch et al., Car2x-based perception in a high-level fusion architecture for cooperative perception systems. Intelligent Vehicles Symposium (IV), 2012 IEEE. IEEE (2012)Google Scholar
  11. 11.
    Y. Bar-Shalom, Update with out-of-sequence measurements in tracking: Exact solution. Aerosp. Electron. Syst. IEEE Trans. 38(3), 769–777 (2002)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.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.BASELABS GmbHChemnitzGermany

Personalised recommendations