Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Abstract

Complex road sections without lane markings cannot constrain vehicles to follow the lane disciplines. As a result, vehicles often exhibit more disorderly rapid lateral movements (RLMs) in these areas, making it difficult to accurately predict vehicle trajectories. This study takes toll plaza diverging area as an example to propose a framework incorporated the Hidden Markov Model (HMM) and Temporal Fusion Transformer (TFT) for vehicle trajectory prediction in non-lane based complex road sections. The results demonstrate that the vehicles exhibit more RLMs when there are more toll lanes matching their toll collection types. Validated on two toll plaza diverging areas with different structures, the proposed framework achieves higher prediction accuracy than other state-of-the-art predictive methods, particularly in long prediction horizons. In addition, the interpretability of TFT suggests that incorporating RLM intention prediction and environmental factors specific to non-lane based areas into trajectory prediction is of great importance.

KEYWORDS:

• Non-lane-based area
• toll plaza diverging area
• rapid lateral movement
• trajectory prediction
• machine learning algorithms

Acknowledgements

The author would like to thank the National Key Research and Development Program of China (No. 2021YFC3001500), the National Natural Science Foundation of China (52102405,52172313), the science and technology innovation Program of Hunan Province (2023RC3143), China Postdoctoral Science Foundation (No.2023M731962), Key Laboratory of Highway Engineering of Ministry of Education (kfj220203), Beijing Natural Science Foundation (L231014). Moreover, thanks to the Automated Roadway Conflicts Identification System (ARCIS) which was developed by the University of Central Florida Smart and Safe Transportation (UCF SST) team.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: Kejun Long, Yi Fei, Lu Xing; data collection: Lu Xing, Ou Zheng; analysis and interpretation of results: Yi Fei, Lu Xing, Kejun Long; draft manuscript preparation: Kejun Long, Yi Fei, Lu Xing; review and editing: Xin Pei, Danya Yao, Mohamed Abdel-Aty. All authors reviewed the results and approved the final version of the manuscript.

Additional information

Funding

This work was supported by National Key Research and Development Program of China (Grant Number 2021YFC3001500), the National Natural Science Foundation of China (Grant Number 52102405,52172313), the science and technology innovation Program of Hunan Province (Grant Number 2023RC3143), China Postdoctoral Science Foundation (Grant Number 2023M731962), Key Laboratory of Highway Engineering of Ministry of Education (Grant Number kfj220203), Beijing Natural Science Foundation (Grant Number L231014).