A Novel Multimodal Vehicle Path Prediction Method Based on Temporal Convolutional Networks

Accurate and reliable prediction of future motions of the nearby agents and effective environment understanding will contribute to high-quality and meticulous path planning for the automated vehicles under uncertainty and guarantee traffic safety for future real-world deployments. This task becomes more challenging in highly dynamic and complex scenarios such as unsignalized intersections where no lights exist to control vehicles behavior, or there are not multiple lines for the vehicles to anticipate drivers’ future intentions based on the lane in which they are driving. In this study, the authors introduce a novel deep learning-based methodology to anticipate vehicles path at unsignalized intersections. The method provides multimodal outputs to take into account the inherited uncertainty and multimodality nature of vehicles behavior. Their proposed model works based on dilated convolutional networks in combination with a mixture density layer. They then cluster various existing mixes into possible paths that are ranked based on probability. They assess the performance and generalization capability of their vehicle path prediction model using several metrics over a large naturalistic dataset containing more than 23800 vehicle trajectories. The obtained results reveal the higher performance of their path prediction approach compared with several baselines and benchmarks.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2022.3151263
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Nasr Azadani, Mozghan
  • Boukerche, Azzedine
• Publication Date: 2022-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 25384-25395
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 12
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Intelligent vehicles; Machine learning; Neural networks; Predictive models; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01881546
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 28 2023 7:24AM