A deep learning framework for modelling left-turning vehicle behaviour considering diagonal-crossing motorcycle conflicts at mixed-flow intersections

With heterogeneous traffic agents moving at unprotected phase, severe crossing conflicts are witnessed at mixed-flow intersections, especially when left-turning vehicles are confronted with motorcycles. However, for modelling vehicle turning behaviour, potential conflicts involving diagonal-crossing motorcycles are seldom investigated in existing studies. To explore these scenes, the authors present a novel interaction-aware deep-learning framework. Firstly, a Long Short-Term Memory (LSTM) based network is employed to encode vehicle historical motion features. Secondly, each vehicle’s potential target lanes are identified with a probabilistic method, followed by a pooling module that extracts and summarizes intention features. Thirdly, Graph Attention Network (GAT) and a synthesized network are introduced to model vehicle-vehicle interaction and vehicle-motorcycle interaction respectively. Finally, multiple kinds of obtained features are sent to a LSTM based decoder module, where both future displacement and body orientation of vehicles are predicted. In short-time simulation experiments, average displacement error is reduced by 47.7% and 20.0% compared to baseline and state-of-the-art methods, with ablation studies conducted to quantify the efficacy of each kind of feature. Moreover, regarding recursive simulation, the authors' model shows availability of reproducing lane-selecting and motorcycle-evasive behaviours. Distributions of post-encroachment time further indicate that the proposed framework can serve as a promising method to realize reliable motion planning for autonomous vehicles.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103415
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21004095
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yao, Ruoyu
  • Zeng, Weiliang
  • Chen, Yihao
  • He, Zhaoshui
• Publication Date: 2021-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 132
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Intersections; Left turns; Machine learning; Motorcycles; Traffic conflicts; Trajectory; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01788454
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 17 2021 2:27PM