Driving Behavior Classification Using a ConvLSTM
This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, inclu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Future Transportation |
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| Online Access: | https://www.mdpi.com/2673-7590/5/2/52 |
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| _version_ | 1849432096309772288 |
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| author | Alberto Pingo João Castro Paulo Loureiro Sílvio Mendes Anabela Bernardino Rolando Miragaia Iryna Husyeva |
| author_facet | Alberto Pingo João Castro Paulo Loureiro Sílvio Mendes Anabela Bernardino Rolando Miragaia Iryna Husyeva |
| author_sort | Alberto Pingo |
| collection | DOAJ |
| description | This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, including data normalization, labeling, and feature extraction, to enhance the model’s performance. By capturing temporal and spatial dependencies within driving patterns, the proposed ConvLSTM model effectively differentiates between normal and aggressive driving behaviors. The model is trained and evaluated against traditional stacked LSTM and Bidirectional LSTM (BiLSTM) architectures, demonstrating superior accuracy and robustness. Experimental results confirm that the preprocessing techniques improve classification performance, ensuring high reliability in driving behavior recognition. The novelty of this work lies in a simple data preprocessing methodology combined with the specific application scenario. By enhancing data quality before feeding it into the AI model, we improve classification accuracy and robustness. The proposed framework not only optimizes model performance but also demonstrates practical feasibility, making it a strong candidate for real-world deployment. |
| format | Article |
| id | doaj-art-e936221535974e67834bbc1c0200e202 |
| institution | Kabale University |
| issn | 2673-7590 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Transportation |
| spelling | doaj-art-e936221535974e67834bbc1c0200e2022025-08-20T03:27:26ZengMDPI AGFuture Transportation2673-75902025-05-01525210.3390/futuretransp5020052Driving Behavior Classification Using a ConvLSTMAlberto Pingo0João Castro1Paulo Loureiro2Sílvio Mendes3Anabela Bernardino4Rolando Miragaia5Iryna Husyeva6School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, PortugalSchool of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, PortugalComputer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, PortugalComputer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, PortugalComputer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, PortugalComputer Science and Communication Research Centre, School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, PortugalComputer Science and Communication Research Centre, National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, 03056 Kyiv, UkraineThis work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, including data normalization, labeling, and feature extraction, to enhance the model’s performance. By capturing temporal and spatial dependencies within driving patterns, the proposed ConvLSTM model effectively differentiates between normal and aggressive driving behaviors. The model is trained and evaluated against traditional stacked LSTM and Bidirectional LSTM (BiLSTM) architectures, demonstrating superior accuracy and robustness. Experimental results confirm that the preprocessing techniques improve classification performance, ensuring high reliability in driving behavior recognition. The novelty of this work lies in a simple data preprocessing methodology combined with the specific application scenario. By enhancing data quality before feeding it into the AI model, we improve classification accuracy and robustness. The proposed framework not only optimizes model performance but also demonstrates practical feasibility, making it a strong candidate for real-world deployment.https://www.mdpi.com/2673-7590/5/2/52artificial intelligenceneural networksLSTMRNNdriving classification |
| spellingShingle | Alberto Pingo João Castro Paulo Loureiro Sílvio Mendes Anabela Bernardino Rolando Miragaia Iryna Husyeva Driving Behavior Classification Using a ConvLSTM Future Transportation artificial intelligence neural networks LSTM RNN driving classification |
| title | Driving Behavior Classification Using a ConvLSTM |
| title_full | Driving Behavior Classification Using a ConvLSTM |
| title_fullStr | Driving Behavior Classification Using a ConvLSTM |
| title_full_unstemmed | Driving Behavior Classification Using a ConvLSTM |
| title_short | Driving Behavior Classification Using a ConvLSTM |
| title_sort | driving behavior classification using a convlstm |
| topic | artificial intelligence neural networks LSTM RNN driving classification |
| url | https://www.mdpi.com/2673-7590/5/2/52 |
| work_keys_str_mv | AT albertopingo drivingbehaviorclassificationusingaconvlstm AT joaocastro drivingbehaviorclassificationusingaconvlstm AT pauloloureiro drivingbehaviorclassificationusingaconvlstm AT silviomendes drivingbehaviorclassificationusingaconvlstm AT anabelabernardino drivingbehaviorclassificationusingaconvlstm AT rolandomiragaia drivingbehaviorclassificationusingaconvlstm AT irynahusyeva drivingbehaviorclassificationusingaconvlstm |