Modular multi sensor data fusion architecture for autonomous vehicles: Using the RACE system on a segway RMP 440

Carsten Simon, Daniel Clarke, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Recent developments in centralized ICT architectures have realized the abstraction of hardware and communications functionality into software within for of highly assisted and autonomous vehicles. This development requires a data-centric approach to the integration and fusion of sensory information within the vehicle. We address this requirement through utilizing a hierarchical framework of intelligent agents. While each agent maintains a well-defined inputs and outputs, the internal processing can be modified to influence the behavior of the system and fulfil the requirements of the application. The hierarchical structure of these agents allows the system to transition from a data-dense environment at the sensor layer to information-dense environment at the application layer. This enables the integration of disparate sensors, system models and sensor fusion in a modular and non-monolithic fashion which is no longer intimately linked to the application.

Original languageEnglish
Title of host publicationProceedings of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479967322
DOIs
StatePublished - 23 Dec 2014
Event2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014 - Beijing, China
Duration: 28 Sep 201430 Sep 2014

Publication series

NameProceedings of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014

Conference

Conference2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
Country/TerritoryChina
CityBeijing
Period28/09/1430/09/14

Keywords

  • Autonomous Parking
  • Multi-Sensor Data Fusion
  • ROS
  • Semantic SLAM

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