A Cognition-Inspired Trajectory Prediction Method for Vehicles in Interactive Scenarios

Trajectory prediction of the ego vehicle is necessary for the cooperation driving of intelligent vehicles and drivers. Methods based on deep learning can fit complex functions, but they usually focus on vehicles' behavioral characteristics. However, vehicles' trajectories are closely related to the cognition results of drivers. Therefore, based on drivers' cognitive characteristics, a network model is designed to predict vehicle trajectories. Specifically, in the perception stage, featured grids are used that are in the driver's view to encode perceptual information; in the decision stage, convolution and graph attention operations are combined to model the driver's interaction with the surrounding traffic elements; in the motion stage, the elements are constrained in one hidden layer by vehicles' actual control inputs and design the corresponding method to obtain probabilistic results. With experiments in two typical scenarios, including intersection and roundabout, the proposed method can obtain reasonable prediction accuracy and generalizability. Meanwhile, abundant experiments are conducted and the results are compared, which reveal some common problems when predicting vehicle trajectories, particularly based on drivers' cognitive characteristics. These lessons learned from this study are summarized which may be useful for newcomers.

• Record URL:
  http://dx.doi.org/10.1049/itr2.12345
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Supplemental Notes:
  • Abstract reprinted with permission of the Institution of Engineering and Technology.
• Authors:
  • Xie, Shanshan
  • Li, Jiachen
  • Wang, Jianqiang
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1544-1559
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 17
  • Issue Number: 8
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Cognition; Driving behavior; Motor vehicles; Neural networks; Predictive models; Trajectory control
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01891363
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 28 2023 9:28AM