Full length article: Information fusion for automotive applications - An overview

  • Authors:

  • Christoph Stiller;Fernando Puente León;Marco Kruse

  • Affiliations:

  • Institut für Mess- und Regelungstechnik, Karlsruhe Institute of Technology (KIT), Engler-Bunte-Ring 21, D-76131 Karlsruhe, Germany;Institut für Industrielle Informationstechnik, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, D-76187 Karlsruhe, Germany;Institut für Industrielle Informationstechnik, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, D-76187 Karlsruhe, Germany

  • Venue:
  • Information Fusion
  • Year:
  • 2011

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Abstract

This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed.