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.


    Access

    Download


    Export, share and cite



    Title :

    Driving Behavior Classification Using a ConvLSTM


    Contributors:


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Stress-Aware Recommendation for Safe Driving using MTL-ConvLSTM

    Bhumika / Vyas, Jayant / Das, Debasis | IEEE | 2022




    Anomaly Detection using IoT Sensor-Assisted ConvLSTM Models for Connected Vehicles

    Zekry, Ahmed / Sayed, Ahmed / Moussa, Mohamed et al. | IEEE | 2021


    Improving Flight Trajectory Predictions with Bayesian-Optimized ConvLSTM Models

    Nghiem, Thi-Lich / Le, Thi-Lan / Le, Viet-Duc et al. | Springer Verlag | 2024