Dual Transformer Based Prediction for Lane Change Intentions and Trajectories in Mixed Traffic Environment

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of vehicles that pose a risk to it. However, due to the uncertainty of human intentions, accurately predicting lane change intentions and trajectories is a great challenge. Therefore, this paper aims to establish the connection between intentions and trajectories and propose a dual Transformer model for the target vehicle. The dual Transformer model contains a lane change intention prediction model and a trajectory prediction model. The lane change intention prediction model is able to extract social correlations in terms of vehicle states and outputs an intention probability vector. The trajectory prediction model fuses the intention probability vector, which enables it to obtain prior knowledge. For the intention prediction model, the accuracy can be improved by designing the multi-head attention. For the trajectory prediction model, the performance can be optimized by incorporating intention probability vectors and adding the LSTM. Verified on NGSIM and highD datasets, the experimental results show that this model has encouraging accuracy. Compared with the model without intention probability vectors, the impact of the model on NGSIM dataset and highD dataset in RMSE is improved by 57.27% and 58.70% respectively. Compared with two existed models, evaluation metrics of the intention prediction can be improved by 7.40-10.09% on NGSIM dataset and 2.17-2.69% on highD dataset within advanced prediction time 1s. This method provides the insights for designing advanced perceptual systems for autonomous vehicles.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2023.3248842
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Gao, Kai
  • Li, Xunhao
  • Chen, Bin
  • Hu, Lin
  • Liu, Jian
  • Du, Ronghua
  • Li, Yongfu
• Publication Date: 2023-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 6203-6216
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 6
  • 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: Autonomous vehicles; Lane changing; Predictive models; Transformers; Vehicle mix; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01896445
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 17 2023 1:42PM