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Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method base...

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Detalles Bibliográficos
Autores principales: Wang, Pangwei, Yu, Hongsheng, Liu, Cheng, Wang, Yunfeng, Ye, Rongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055911/
https://www.ncbi.nlm.nih.gov/pubmed/36991658
http://dx.doi.org/10.3390/s23062950
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author Wang, Pangwei
Yu, Hongsheng
Liu, Cheng
Wang, Yunfeng
Ye, Rongsheng
author_facet Wang, Pangwei
Yu, Hongsheng
Liu, Cheng
Wang, Yunfeng
Ye, Rongsheng
author_sort Wang, Pangwei
collection PubMed
description Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety.
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spelling pubmed-100559112023-03-30 Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios Wang, Pangwei Yu, Hongsheng Liu, Cheng Wang, Yunfeng Ye, Rongsheng Sensors (Basel) Article Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety. MDPI 2023-03-08 /pmc/articles/PMC10055911/ /pubmed/36991658 http://dx.doi.org/10.3390/s23062950 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Pangwei
Yu, Hongsheng
Liu, Cheng
Wang, Yunfeng
Ye, Rongsheng
Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios
title Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios
title_full Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios
title_fullStr Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios
title_full_unstemmed Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios
title_short Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios
title_sort real-time trajectory prediction method for intelligent connected vehicles in urban intersection scenarios
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055911/
https://www.ncbi.nlm.nih.gov/pubmed/36991658
http://dx.doi.org/10.3390/s23062950
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