Route and Stopping Intent Prediction at Intersections From Car Fleet Data

In this paper, an approach is presented to predict the route and stopping intent of human-driven vehicles at urban intersections using a selection of distinctive features observed on the vehicle state (position, heading, acceleration, velocity). For potential future advanced driver assistance systems, this can facilitate the situation analysis and risk assessment at road intersections, helping to improve the protection of vulnerable road users. After extracting recorded driving data for nine intersections (featuring over 50 000 crossings) from a database, they are assigned to possible routes and transformed from a time-based representation to a distance-based one. Using random decision forests, the route intent can be predicted with a mean unweighted average recall (UAR) of 0.76 at 30 m before the relevant intersection center, the stopping intent prediction scores a mean UAR of 0.78.

• Record URL:
  https://doi.org/10.1109/TIV.2016.2617625
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2016, IEEE.
• Authors:
  • Gross, Florian
  • Jordan, Justus
  • Weninger, Felix
  • Klanner, Felix
  • Schuller, Bjorn
• Publication Date: 2016-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 177-186
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 1
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Data mining; Driver support systems; Intersections; Mathematical prediction; Risk assessment; Stopping; Vehicle fleets
• Uncontrolled Terms: Intentions
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01623208
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 24 2017 3:15PM