Discretionary Cut-In Driving Behavior Risk Assessment Based on Naturalistic Driving Data

Cut-in driving behavior is one of the basic micro traffic actions for vehicles. A risk assessment helps vehicles execute the behavior well and determine how to react to the same maneuver from other traffic participants when following a leading car or truck. This article presents a discretionary cut-in driving behavior risk assessment method based on field driving data and a united algorithm that is designed to a combine decision tree and a support vector machine to achieve enhanced sensitivity for the riskiest traffic conditions. To build the learning database, a wavelet method is employed to filter naturalistic driving data, incorporating the K-means approach. An unsupervised data learning method is used to categorize the impact on vehicles in the target lane, indicated by a target vehicle’s average and maximum deceleration, into three groups. Experiment results based on self-collected and public databases show that tested vehicles are aware of the risk presented by other cars’ and trucks’ cut-in driving as well as their own impact on traffic participants in the target lane.

• Record URL:
  • https://doi.org/10.1109/MITS.2021.3093349
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19391390
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Gao, Hongbo
  • 0000-0002-5565-0284
  • Hu, Chuan
  • Xie, Guatao
  • Han, Chao
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 29-40
• Serial:
  • IEEE Intelligent Transportation Systems Magazine
  • Volume: 14
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1939-1390
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Behavior; Braking; Driving; Lane changing; Risk assessment; Vehicle dynamics
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01847364
• Record Type: Publication
• Files: TRIS
• Created Date: May 26 2022 4:59PM