• DocumentCode
    2758061
  • Title

    U&I Aware: A Framework Using Data Mining and Collision Detection to Increase Awareness for Intersection Users

  • Author

    Salim, Flora Dilys ; Loke, Seng Wai ; Rakotonirainy, Andry ; Krishnaswamy, Shonali

  • Author_Institution
    Caulfield Sch. of Inf. Technol., Monash Univ., Clayton, VIC
  • Volume
    2
  • fYear
    2007
  • fDate
    21-23 May 2007
  • Firstpage
    530
  • Lastpage
    535
  • Abstract
    An intersection safety system should adapt to the particular characteristics that identify an intersection, by mining traffic and collision data. Given the large amount of sensor data that are obtained for intersections and from sensor-equipped cars, analysis and learning of such data is essential. This paper presents a new method to improve safety at intersections using a combination of a mathematical based collision detection algorithm and data mining. A number of scenarios at a simulated intersection are explored with encouraging results from our data mining implementation. The results suggest that our approach can help improve situation awareness and automate understanding of intersections, which, in turn, can be used to increase safety at intersections.
  • Keywords
    automobiles; collision avoidance; data mining; learning (artificial intelligence); road accidents; road safety; road traffic; traffic information systems; U&I aware; collision detection algorithm; data learning; intersection collision warning system; intersection safety system; intersection user awareness; sensor data; sensor-equipped car; traffic data mining; ubiquitous intersection awareness; Alarm systems; Collision avoidance; Cooperative systems; Data mining; Road accidents; Road safety; Robot sensing systems; Robotics and automation; Transportation; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Information Networking and Applications Workshops, 2007, AINAW '07. 21st International Conference on
  • Conference_Location
    Niagara Falls, Ont.
  • Print_ISBN
    978-0-7695-2847-2
  • Type

    conf

  • DOI
    10.1109/AINAW.2007.360
  • Filename
    4224158