Weight Semi Hidden Markov Model and Driving Situation Classification for Driver Behavior Diagnostic

In this study, the author proposes to use statistical modelling to analyze, model, and categorize driving activity. To achieve this objective, he develops a new statistical model by adding a weight feature to the classic Semi Hidden Markov Model (SHMM) framework. Then, to assess its capacity, he conducts an experiment that allows him to record 718 driving sequences categorized in 36 situations. He then used his modelling to identify the driver's aim and the driving situation he's in. Furthermore, he adapted the ascendant hierarchic classification technique to this modelling. It allows him to understand which situations are close and to define partitions of whole driving situations. Finally, on these sequences, his modelling choice allows him to predict the driver’s situation with, on average, an 85% success rate. These results show the HMM effectiveness to manage temporal and multidimensional data by modelling predicting drivers’ behavior.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780874141580
• Corporate Authors:

  University of Iowa, Iowa City

  Public Policy Center
  227 South Quadrangle
  Iowa City, IA  United States  52242-1192
• Authors:
  • Dapzol, Nicolas
• Conference:
  • Driving Assessment 2007: 4th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design
  • Location: Stevenson Washington, United States
  • Date: 2007-7-9 to 2007-7-12
• Publication Date: 2007

Language

• English

Media Info

• Media Type: CD-ROM
• Features: Figures; References;
• Pagination: pp 84-90
• Monograph Title: Proceedings of the 4th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Stevenson, Washington, July 9-12, 2007

Subject/Index Terms

• TRT Terms: Behavior; Classification; Drivers; Driving; Markov processes; Mathematical prediction; Statistical analysis; Traffic models
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01055492
• Record Type: Publication
• ISBN: 9780874141580
• Files: TRIS
• Created Date: Aug 23 2007 1:01PM