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A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles

The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the...

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Autores principales: Du, Luyao, Chen, Wei, Ji, Jing, Pei, Zhonghui, Tong, Bingming, Zheng, Hongjiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786501/
https://www.ncbi.nlm.nih.gov/pubmed/35082845
http://dx.doi.org/10.1155/2022/9516218
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author Du, Luyao
Chen, Wei
Ji, Jing
Pei, Zhonghui
Tong, Bingming
Zheng, Hongjiang
author_facet Du, Luyao
Chen, Wei
Ji, Jing
Pei, Zhonghui
Tong, Bingming
Zheng, Hongjiang
author_sort Du, Luyao
collection PubMed
description The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV's lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.
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spelling pubmed-87865012022-01-25 A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles Du, Luyao Chen, Wei Ji, Jing Pei, Zhonghui Tong, Bingming Zheng, Hongjiang Comput Intell Neurosci Research Article The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV's lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely. Hindawi 2022-01-17 /pmc/articles/PMC8786501/ /pubmed/35082845 http://dx.doi.org/10.1155/2022/9516218 Text en Copyright © 2022 Luyao Du et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Du, Luyao
Chen, Wei
Ji, Jing
Pei, Zhonghui
Tong, Bingming
Zheng, Hongjiang
A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles
title A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles
title_full A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles
title_fullStr A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles
title_full_unstemmed A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles
title_short A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles
title_sort novel intelligent approach to lane-change behavior prediction for intelligent and connected vehicles
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786501/
https://www.ncbi.nlm.nih.gov/pubmed/35082845
http://dx.doi.org/10.1155/2022/9516218
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