NDLI: A sensor independent probabilistic fusion system for driver assistance systems

Content Provider	IEEE Xplore Digital Library
Author	Munz, M. Dietmayer, K. Mahlisch, M.
Copyright Year	2009
Description	Author affiliation: Institute of Measurement, Control, and Microtechnology, University of Ulm, Germany (Munz, M.; Dietmayer, K.) \|\| Department Environment Perception (GR/PAP), Daimler AG, Ulm, Germany (Mahlisch, M.)
Abstract	In this contribution we present a probabilistic fusion framework for implementing a sensor independent measurement fusion. All interfaces are using probabilistic descriptions of measurement and existence uncertainties. We introduce several extensions to already existing algorithms: the support for association of multiple measurements to the same object is introduced, which reduces the effects of split segments in the data preprocessing step of high-resolution sensors like laser scanners. Furthermore, we present an approach for integrating explicit object birth models. We also developed extensions to speed up the algorithm which lead to real-time performance with fragmented data. We show the application of the framework in an automotive multi-target multi-sensor environment by fusing laser scanner and video. The algorithms were evaluated using real-world data in our research vehicle.
Starting Page	1
Ending Page	6
File Size	2347352
Page Count	6
File Format	PDF
ISBN	9781424455195
DOI	10.1109/ITSC.2009.5309845
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensor fusion Sensor systems Technological innovation Intelligent transportation systems Intelligent sensors Laser modes Predictive models System testing State estimation USA Councils
Content Type	Text
Resource Type	Article

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