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Published in:
Introduction to Automated Driving Read chapter Read first chapter

2017 | OriginalPaper | Chapter

7. The Role of Multisensor Environmental Perception for Automated Driving

Authors : Robin Schubert, Marcus Obst

Published in: Automated Driving

Publisher: Springer International Publishing

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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.

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previous chapter Vehicle Reference Lane Calculation for Autonomous Vehicle Guidance Control
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Footnotes
1
To some extent, this can be compared to hardware abstraction layers used in programming to support modular software architectures that facilitate reusability.
 
2
There are also other variants of grid implementations which consider more dimensions such as the 4D grid [8].
 
3
Save by high-resolution sensors such as lidars in close distances as a result of a clustering process.
 
4
For instance, PreScan by TASS International or Carmaker by IPG
 
Literature
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B. Khaleghi et al., Multisensor data fusion: A review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)CrossRef
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R. Schubert et al., Empirical evaluation of vehicular models for ego motion estimation, in Intelligent Vehicles Symposium (IV), 2011 IEEE. IEEE (2011)
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S. Thrun et al., Probabilistic Robotics (The MIT Press, Cambridge, 2005)
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P.C. Mahalanobis, On the generalised distance in statistics. Proc. Natl. Inst. Sci. India 2(1), 49–55 (1936)MathSciNet
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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
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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
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C. Coué et al., Bayesian occupancy filtering for multitarget tracking: An automotive application. Int. J. Robot. Res. 25(1), 19–30 (2006)CrossRef
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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)
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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)
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Y. Bar-Shalom, Update with out-of-sequence measurements in tracking: Exact solution. Aerosp. Electron. Syst. IEEE Trans. 38(3), 769–777 (2002)CrossRef
Metadata
Title
The Role of Multisensor Environmental Perception for Automated Driving
Authors
Robin Schubert
Marcus Obst
Copyright Year
2017
Book
Automated Driving

Print ISBN: 978-3-319-31893-6

Electronic ISBN: 978-3-319-31895-0

Copyright Year: 2017

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

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