Study of Longitudinal Driving Behavior Using Advanced Data Collection and Analysis Platform

For the evaluation of in-vehicle driving assistance systems and to determine the optimal parameter values for the control algorithm, relevant data of driver behavior and performance need to be collected and analyzed efficiently. The paper describes the design and the (hardware and software) architecture of an experimental platform for driver behavioral data collection and analysis. An instrumented vehicle test bed was implemented to measure the information on driver actions and vehicle states, and a special data processing program was developed to review, extract and analyze the captured data. In addition, some preliminary results of a longitudinal driving behavior study in P.R. China using the developed experimental platform are presented. Thirty-three participates were involved in the first phase of the study. Behavioral data were collected on different road categories in Beijing, and were analyzed based on the developed analysis method and data processing program. It is demonstrated that the developed platform is a reliable and robust tool for collection and analysis of data concerning driving behavior under real-world circumstances. In this research, three hypotheses have been investigated: (1) differences of longitudinal driving behavior between genders, between ages, and between number of years of driving experience; (2) longitudinal driving behavior varies with road categories; and (3) longitudinal driving behavior varies with driving style and driver characteristics. We conclude that hypothesis (1) is rejected, and hypotheses (2) and (3) are confirmed.

Language

  • English

Media Info

  • Media Type: DVD
  • Features: Figures; References; Tables;
  • Pagination: 16p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01156988
  • Record Type: Publication
  • Report/Paper Numbers: 10-1472
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 10:39AM