Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

The Role of Multisensor Environmental Perception for Automated Driving

  • Chapter
  • First Online:
Automated Driving

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.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    To some extent, this can be compared to hardware abstraction layers used in programming to support modular software architectures that facilitate reusability.

  2. 2.

    There are also other variants of grid implementations which consider more dimensions such as the 4D grid [8].

  3. 3.

    Save by high-resolution sensors such as lidars in close distances as a result of a clustering process.

  4. 4.

    For instance, PreScan by TASS International or Carmaker by IPG

References

  1. B. Khaleghi et al., Multisensor data fusion: A review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)

    Article  Google Scholar 

  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. S. Thrun et al., Probabilistic Robotics (The MIT Press, Cambridge, 2005)

    Google Scholar 

  4. P.C. Mahalanobis, On the generalised distance in statistics. Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936)

    MathSciNet  Google Scholar 

  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 2002

    Google Scholar 

  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. 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

    Article  Google Scholar 

  8. C. Coué et al., Bayesian occupancy filtering for multitarget tracking: An automotive application. Int. J. Robot. Res. 25(1), 19–30 (2006)

    Article  Google Scholar 

  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. 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. Y. Bar-Shalom, Update with out-of-sequence measurements in tracking: Exact solution. Aerosp. Electron. Syst. IEEE Trans. 38(3), 769–777 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. BASELABS GmbH, Chemnitz, Germany

    Robin Schubert & Marcus Obst

Authors
  1. Robin Schubert
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcus Obst
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robin Schubert .

Editor information

Editors and Affiliations

  1. Institute of Electrical Measurement and Measurement Signal Processing, Virtual Vehicle Research Center and Graz University of Technology, Graz, Austria

    Daniel Watzenig

  2. Institute of Automation and Control, Graz University of Technology, Graz, Austria

    Martin Horn

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Schubert, R., Obst, M. (2017). The Role of Multisensor Environmental Perception for Automated Driving. In: Watzenig, D., Horn, M. (eds) Automated Driving. Springer, Cham. https://doi.org/10.1007/978-3-319-31895-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31895-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31893-6

  • Online ISBN: 978-3-319-31895-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation