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Research Projects

Intelligent Transportation Systems

Principal Investigators: Catherine Deegan; Simon McLoughlin
Contact Details: Email:
Phone: +353 (0) 1 8851093
Phone: +353 (0) 1 8851343
Collaborators: National Roads Authority (NRA), Ireland

Research Area Description:

The ITS group at the Institute of Technology Blanchardstown have been working on problems in the area of automated analysis of road signage and delineation since 2002. In conjunction with the National Roads Authority (NRA) of Ireland, a vehicular mobile mapping system has been developed to address specific problems in this area. The system is composed of a number of navigation sensors for geo-referencing (positioning) and a multi-camera (stereo) system for automated feature extraction from imagery. Previous projects have focused on road sign detection for inventory and analysis, the detection of defective road studs (or cat eyes) and sensor integration. Current projects are exploring the use of the system as an autonomous mobile retroreflectometer, i.e. a platform to evaluate the retroreflectivity of delineation and signage in a manner that is fully automated and carried out at normal driving speeds. The academic research interests of the group lie in areas such as computer vision/image processing, photometry, mobile mapping, navigation, and in particular, how these areas can be applied in the Intelligent Transportation Systems domain. The group is/has been supported by the Higher Education Authority (HEA), Technological Sector Research Fund for the Institutes of Technology Ireland (IOTI) and the National Roads Authority (NRA) of Ireland.

Current Research Projects:
  • Simon Murray (NRA Fellowship PhD): Image Based Analysis of retroreflective road markings
  • Sean Haughey (NRA Fellowship MEng): Sensor Integration for Mobile Mapping and automated feature analysis.
Past research Projects:
  • Stephen Foy (Strand 1 MEng): Integration of INS with GPS for Mobile Mapping Applications.
  • Ciara Mulvihill (Strand 1 MSc): Automatic Assessment or Raised Pavement Marker integrity using Machine Vision

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