A Physical Law Constrained Deep Learning Model for Vehicle Trajectory Prediction

Vehicle trajectory prediction is crucial and indispensable for ensuring the safe and efficient operation of autonomous vehicles in complex traffic environments. The application of IoT technology in the collaborative automated driving system (CADS) has established a robust data foundation for vehicle...

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Vydáno v:IEEE internet of things journal Ročník 10; číslo 24; s. 1
Hlavní autoři: Li, Hanchu, Liao, Ziyi, Rui, Yikang, Li, Linchao, Ran, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Behavioral sciences
Data models
Data-driven
Deep learning
Driving
Force
GRU encoder-decoder framework
Internet of Things
Mathematical models
Physical-driven
Predictive models
Social force rules
Trajectories
Trajectory
Vehicle trajectory prediction
Vehicles
ISSN:2327-4662, 2327-4662
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Shrnutí:Vehicle trajectory prediction is crucial and indispensable for ensuring the safe and efficient operation of autonomous vehicles in complex traffic environments. The application of IoT technology in the collaborative automated driving system (CADS) has established a robust data foundation for vehicle trajectory prediction. Accurate prediction requires not only a substantial amount of high-quality data but also a deep understanding of the vehicles driving characteristics and interactions between neighboring vehicles. To enhance the study of vehicle trajectory prediction, this paper proposes a novel Social Force-constrained Gated Recurrent Unit (SF-GRU) model, which integrates data-driven and physics-driven models. Specifically, the SF-GRU model is based on the GRU encoder-decoder framework and incorporates Social Force constraints to enhance and supplement the model input based on vehicle time-series trajectory data, which describes the driving and interactive behaviors of vehicles during driving, as well as the interactions between neighboring vehicles and the surrounding environment. The model is trained and validated using the NGSIM dataset. Experimental results demonstrate that the SF-GRU model outperforms existing state-of-the-art models in both longitudinal and lateral motion, and that social force constraints are more effective than spatial variables in improving prediction accuracy. Furthermore, the SF-GRU model can intuitively and accurately consider the interactions between vehicles, and precisely describe the changes of relevant variables in the prediction process, thus enhancing the interpretability of the data-driven model. The SF-GRU model has great potential in vehicle trajectory prediction and can provide important support for the practical implementation of autonomous driving vehicles.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3305395