journal article Open Access Feb 04, 2024

Multi-modality trajectory prediction with the dynamic spatial interaction among vehicles under connected vehicle environment

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Abstract
AbstractCompared to non-connected vehicle environments, the connected vehicle environment establishes vehicle interconnection through communication technologies, resulting in more complex interaction, network topologies, and large-scale inputs. This complexity renders traditional trajectory prediction models, which rely primarily on inputting historical information of the target vehicle, inadequate for handling the complex and dynamic interactive lane-changing scenarios in connected vehicle environments. In a connected vehicle environment, it is necessary to propose a more targeted and stable lane-changing behavior prediction method based on vehicle traveling characteristics. Taking into account dynamic spatial interaction among vehicles, this study proposes a multi-modality trajectory prediction model called STA-LSTM to perform analysis on the potential interactive behaviors among vehicles under connected vehicle lane-changing scenarios, and specifically to expand the multi-modality feature input of the vehicle trajectory prediction model. The spatial grid occupancy method is used to model the interactions between vehicles. A space-dimensional attention mechanism is introduced to adaptively match the influencing weights of the surrounding vehicles with the target vehicle and to improve the interactive information extraction method. In addition, the attention module is incorporated into the LSTM decoder from the time dimension so that the established model can identify significant historical hidden features during each trajectory decoding process. To account for the uncertainty of trajectory prediction, the vectors of vehicle interactions are incorporated into contextual information to improve the reliability of prediction results and the robustness of the established model. Compared with conventional baseline models, the proposed model exhibited lower root mean square error (RMSE) and negative log-likelihood (NLL) values, and the RMSE values at different prediction times of 1s, 2s, 3s, 4s, and 5s are 0.46m, 1.15m, 1.89m, 2.84m, and 4.05m, respectively. This indicates that the proposed model can accurately predict the interactions between vehicles and the travel paths of surrounding target vehicles.
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Details
Published
Feb 04, 2024
Vol/Issue
14(1)
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Funding
National Natural Science Foundation of China Award: 52072333
Science and Technology Project of Hebei Education Department Award: BJK2023026
Hebei Natural Science Foundation Award: F2022203054
Cite This Article
Lisheng Jin, Xingchen Liu, Yinlin Wang, et al. (2024). Multi-modality trajectory prediction with the dynamic spatial interaction among vehicles under connected vehicle environment. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-53315-6