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Vehicle Interaction Behavior Prediction with Self-Attention

The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model...

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Detalles Bibliográficos
Autores principales: Li, Linhui, Sui, Xin, Lian, Jing, Yu, Fengning, Zhou, Yafu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779130/
https://www.ncbi.nlm.nih.gov/pubmed/35062390
http://dx.doi.org/10.3390/s22020429
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author Li, Linhui
Sui, Xin
Lian, Jing
Yu, Fengning
Zhou, Yafu
author_facet Li, Linhui
Sui, Xin
Lian, Jing
Yu, Fengning
Zhou, Yafu
author_sort Li, Linhui
collection PubMed
description The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios.
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spelling pubmed-87791302022-01-22 Vehicle Interaction Behavior Prediction with Self-Attention Li, Linhui Sui, Xin Lian, Jing Yu, Fengning Zhou, Yafu Sensors (Basel) Article The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lane gap. In addition, the proposed model utilizes the sliding window algorithm to extract VC behavior information. Then the temporal characteristics of the three interactive features mentioned above will be caught by two layers of self-attention encoder with six heads respectively. Finally, target vehicle’s future behavior will be predicted by a sub-network consists of a fully connected layer and SoftMax module. The experimental results show that this method has achieved accuracy, precision, recall, and F1 score of more than 92% and time to event of 2.9 s on a Next Generation Simulation (NGSIM) dataset. It accurately predicts the interactive behaviors in class-imbalance prediction and adapts to various driving scenarios. MDPI 2022-01-07 /pmc/articles/PMC8779130/ /pubmed/35062390 http://dx.doi.org/10.3390/s22020429 Text en © 2022 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
Li, Linhui
Sui, Xin
Lian, Jing
Yu, Fengning
Zhou, Yafu
Vehicle Interaction Behavior Prediction with Self-Attention
title Vehicle Interaction Behavior Prediction with Self-Attention
title_full Vehicle Interaction Behavior Prediction with Self-Attention
title_fullStr Vehicle Interaction Behavior Prediction with Self-Attention
title_full_unstemmed Vehicle Interaction Behavior Prediction with Self-Attention
title_short Vehicle Interaction Behavior Prediction with Self-Attention
title_sort vehicle interaction behavior prediction with self-attention
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779130/
https://www.ncbi.nlm.nih.gov/pubmed/35062390
http://dx.doi.org/10.3390/s22020429
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AT yufengning vehicleinteractionbehaviorpredictionwithselfattention
AT zhouyafu vehicleinteractionbehaviorpredictionwithselfattention