Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 16; no. 10; p. 1668
Main Authors: Elfring, Jos, Appeldoorn, Rein, Van den Dries, Sjoerd, Kwakkernaat, Maurice
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 11.10.2016
MDPI
Subjects:
Algorithms
automated driving
Automation
Computer programs
Construction
Data fusion
Data integration
Methodology
multisensor
R&D
Research & development
Sensors
Software
Software upgrading
Vehicles
world modeling
Netherlands
data fusion
multisensor
automated driving
world modeling
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s16101668