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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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
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Abstract	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.
AbstractList	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. 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.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.
Author	Van den Dries, Sjoerd Elfring, Jos Appeldoorn, Rein Kwakkernaat, Maurice
AuthorAffiliation	2 Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands; svddries@smart-robotics.nl 1 Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond 5700 AT, The Netherlands; rein.appeldoorn@tno.nl (R.A.); maurice.kwakkernaat@tno.nl (M.K.)
AuthorAffiliation_xml	– name: 2 Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands; svddries@smart-robotics.nl – name: 1 Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond 5700 AT, The Netherlands; rein.appeldoorn@tno.nl (R.A.); maurice.kwakkernaat@tno.nl (M.K.)
Author_xml	– sequence: 1 givenname: Jos surname: Elfring fullname: Elfring, Jos – sequence: 2 givenname: Rein surname: Appeldoorn fullname: Appeldoorn, Rein – sequence: 3 givenname: Sjoerd surname: Van den Dries fullname: Van den Dries, Sjoerd – sequence: 4 givenname: Maurice surname: Kwakkernaat fullname: Kwakkernaat, Maurice
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StartPage	1668
SubjectTerms	Algorithms automated driving Automation Computer programs Construction Data fusion Data integration Methodology multisensor R&D Research & development Sensors Software Software upgrading Vehicles world modeling
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Title	Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving
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Volume	16
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