Classification for Safety-Critical Car-Cyclist Scenarios Using Machine Learning

The number of fatal car-cyclist accidents is increasing. Advanced Driver Assistance Systems (ADAS) can improve the safety of cyclists, but they need to be tested with realistic safety-critical car-cyclist scenarios. In order to store only relevant scenarios, an online classification algorithm is needed. The authors demonstrate that machine learning techniques can be used to detect and classify those scenarios based on their trajectory data. A dataset consisting of 99 realistic car-cyclist scenarios is gathered using an instrumented vehicle. The authors achieved a classification accuracy of the gathered data of 87.9%. The execution time of only 45.8 us shows that the algorithm is suitable for online purposes.

• Record URL:
  https://doi.org/10.1109/ITSC.2015.323
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9781467365956
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Corporate Authors:

  Institute of Electrical and Electronics Engineers (IEEE)

  3 Park Avenue, 17th Floor
  New York, NY  United States  10016-5997
• Authors:
  • Cara, Irene
  • de Gelder, Erwin
• Conference:
  • 18th International IEEE Conference on Intelligent Transportation Systems (ITSC)
  • Location: Canary Islands , Spain
  • Date: 2015-9-15 to 2015-9-18
• Publication Date: 2015

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1995-2000
• Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

• TRT Terms: Algorithms; Classification; Cyclists; Machine learning; Traffic conflicts; Trajectory
• Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Safety and Human Factors;

Filing Info

• Accession Number: 01603011
• Record Type: Publication
• ISBN: 9781467365956
• Files: TRIS
• Created Date: Jun 28 2016 4:16PM