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Open AccessArticle

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond 5700 AT, The Netherlands
Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Sensors 2016, 16(10), 1668;
Received: 22 July 2016 / Revised: 2 September 2016 / Accepted: 26 September 2016 / Published: 11 October 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture. View Full-Text
Keywords: multisensor; data fusion; world modeling; automated driving multisensor; data fusion; world modeling; automated driving
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MDPI and ACS Style

Elfring, J.; Appeldoorn, R.; Van den Dries, S.; Kwakkernaat, M. Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving. Sensors 2016, 16, 1668.

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