Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication

Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-...

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Autores principales:	Seong Kyung Kwon, Ji Hwan Seo, Jun Young Yun, Kyoung-Dae Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	CNN-LSTM driving behavior classification driving safety Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/091477a9460c40bdbdd7552eea966142
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id	oai:doaj.org-article:091477a9460c40bdbdd7552eea966142
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spelling	oai:doaj.org-article:091477a9460c40bdbdd7552eea9661422021-11-11T15:24:07ZDriving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication10.3390/app1121104202076-3417https://doaj.org/article/091477a9460c40bdbdd7552eea9661422021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10420https://doaj.org/toc/2076-3417Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle.Seong Kyung KwonJi Hwan SeoJun Young YunKyoung-Dae KimMDPI AGarticleCNN-LSTMdriving behavior classificationdriving safetyTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10420, p 10420 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CNN-LSTM driving behavior classification driving safety Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	CNN-LSTM driving behavior classification driving safety Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Seong Kyung Kwon Ji Hwan Seo Jun Young Yun Kyoung-Dae Kim Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
description	Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle.
format	article
author	Seong Kyung Kwon Ji Hwan Seo Jun Young Yun Kyoung-Dae Kim
author_facet	Seong Kyung Kwon Ji Hwan Seo Jun Young Yun Kyoung-Dae Kim
author_sort	Seong Kyung Kwon
title	Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
title_short	Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
title_full	Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
title_fullStr	Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
title_full_unstemmed	Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
title_sort	driving behavior classification and sharing system using cnn-lstm approaches and v2x communication
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/091477a9460c40bdbdd7552eea966142
work_keys_str_mv	AT seongkyungkwon drivingbehaviorclassificationandsharingsystemusingcnnlstmapproachesandv2xcommunication AT jihwanseo drivingbehaviorclassificationandsharingsystemusingcnnlstmapproachesandv2xcommunication AT junyoungyun drivingbehaviorclassificationandsharingsystemusingcnnlstmapproachesandv2xcommunication AT kyoungdaekim drivingbehaviorclassificationandsharingsystemusingcnnlstmapproachesandv2xcommunication
_version_	1718435348909916160

Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication

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