Abstract
The work proposes an intelligent estimation system to classify dangerous driving turnings on roads where consider the dynamics of the vehicle. A convolutional neural network (CNN) and a long short-term memory (LSTM) model are applied. The vehicle’s dynamic characteristics are measured by the use of inertial sensors incorporated in the vehicle. The actual data gathered from CAN (Controller Area Network) bus, accelerations measures, gyroscope readings, and steering angle are applied to determine the classification of conduction operations. Urban, rural and motorway roads located in the south region of Germany are used in this investigation as real scenarios. The findings achieved with the suggested neural models are encouraging and indicate that this artificial intelligence approach may indeed be used to alert user of unsafe or risky behavior in real time, with the aim of making driving more comfortable and safer.
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Barreno, F., Santos, M., Romana, M. (2023). Vehicle Warning System Based on Road Curvature Effect Using CNN and LSTM Neural Networks. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_25
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