title: Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving.
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.
authors.affiliation: Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond 5700 AT, The Netherlands. jos.elfring@tno.nl.
authors.forename: Jos
authors.initials: J
authors.lastname: Elfring
authors.affiliation: Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond 5700 AT, The Netherlands. rein.appeldoorn@tno.nl.
authors.forename: Rein
authors.initials: R
authors.lastname: Appeldoorn
authors.affiliation: Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands. svddries@smart-robotics.nl.
authors.forename: Sjoerd
authors.initials: S
authors.lastname: van den Dries
authors.affiliation: Integrated Vehicle Safety department, Netherlands Organization for Applied Scientific Research TNO, Helmond 5700 AT, The Netherlands. maurice.kwakkernaat@tno.nl.
authors.forename: Maurice
authors.initials: M
authors.lastname: Kwakkernaat
keywords: automated driving; data fusion; multisensor; world modeling