Trajectory Prediction of Preceding Target Vehicles Based on Lane Crossing and Final Points Generation Model Considering Driving Styles

Xulei Liu, Yafei Wang, Zhisong Zhou, Kanghyun Nam, Chongfeng Wei, Chengliang Yin

Research output: Contribution to journalArticlepeer-review

Abstract

Reliable trajectory prediction of preceding target vehicles (PTVs) is crucial for the planning and decision making of automated vehicles. However, the future trajectories are affected by the driver's intention and diverse driving styles, which can hardly be predicted precisely, especially when the vehicle performs a lane change maneuver. In this study, we propose a lane crossing and final points generation (CFPG) model-based trajectory prediction approach for PTVs, in which the key influence factors such as the driver's intention and the mixed driving styles are included. Firstly, we build a maneuver and stage recognition model upon the long short term memory (LSTM) to infer the current maneuver of the preceding target vehicle. Furthermore, this approach predicts the lane crossing point using a physics-based model combining with a deep conditional generative model trained by a deep neural network. Moreover, a maneuver-based model is adopted to predict the final point according to the prediction interval. In order to avoid the possible cumulative error caused by iteratively generating trajectories in traditional methods, we use a curve fitting method to obtain the predicted trajectory. Lane changing data collected from naturalistic driving scenarios are used to verify the proposed approach, and the results suggest more accurate and reliable prediction trajectories compared with conventional methods.

Original languageEnglish
JournalIEEE Transactions on Vehicular Technology
Early online date21 Jul 2021
DOIs
Publication statusEarly online date - 21 Jul 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
IEEE

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Automated vehicles
  • Deep learning
  • deep neural network
  • Kinematics
  • lane change
  • Predictive models
  • Task analysis
  • Trajectory
  • Uncertainty
  • vehicle trajectory prediction
  • Vehicles

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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