Sensor Fusion for Predicting Vehicle's Path for Collision Avoidance Systems

This paper discusses sensor fusion for collision warning (CW) that uses a hierarchical-structured algorithm in order to provide path prediction for the ego-vehicle in such situations. The sensor fusion combines traffic environment data with car dynamics. The output space of the projected path in the researcher’s system is three-dimensional and it was shown that the system estimates a 3-4 second lead in the path with an accuracy of up to 50 centimeters. Both vehicle data and road boundaries (RB) data are compared with motion models and road boundaries models, respectively. The two sets of data that have been turned into route predictions are then fused using a priori knowledge models as well as online situation models. Real-world data and various traffic situations are presented as verification for the presented algorithmic models.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Authors:
  • Polychronopoulos, Aris
  • Tsogas, Manolis
  • Amditis, Angelos J
  • Andreone, Luisa
• Publication Date: 2007-9

Language

• English

Media Info

• Media Type: Print
• Features: Appendices; Figures; Photos; References; Tables;
• Pagination: pp 549-562
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 8
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous intelligent cruise control; Crash avoidance systems; Data fusion; Dynamic models; Intelligent transportation systems
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; I73: Traffic Control;

Filing Info

• Accession Number: 01080250
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: BTRIS, TRIS
• Created Date: Oct 31 2007 6:38AM