Utilizing Random Forest and Neural Network to Extract Lane Change Events on Shanghai Highway

This paper considers a comprehensive naturalistic driving experiment to collect driving data about vehicle’s lane change behaviors on an actual Shanghai highway. Using the acquired real-world natural driving data, a lane-change database with kinds of vehicles’ driving information is established. A novel way of selecting variables is proposed by considering the values and changes of various variables over time periods. The random forest (RF) and Gini coefficient are adopted for variables’ importance ranking, and a total of seven top contributing factors are selected. The results indicate that the variables concerning vehicles’ acceleration and speed, especially the standard deviation of longitudinal acceleration, exerts the greatest influence. Subsequently, based on these key factors, the long short term memory (LSTM) is utilized for performing training and predicting operations on the collected naturalistic driving data. Finally, good effects and high accuracy on the recognition and extraction of lane change behavior are obtained.

• Record URL:
  https://doi.org/10.1061/9780784482292.031
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482292
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Wang, Gang
  • Sun, Ping
  • Zhang, Yi
• Conference:
  • 19th COTA International Conference of Transportation Professionals
  • Location: Nanjing , China
  • Date: 2019-7-6 to 2019-7-8
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

• TRT Terms: Acceleration (Mechanics); Automatic data collection systems; Behavior; Drivers; Lane changing; Neural networks; Speed
• Geographic Terms: Shanghai (China)
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01716070
• Record Type: Publication
• ISBN: 9780784482292
• Files: TRIS, ASCE
• Created Date: Sep 13 2019 9:44AM