Ponencia
Estimation of the Optimum Speed to Minimize the Driver Stress Based on the Previous Behavior
Autor/es | Corcoba Magaña, Victor
Muñoz Organero, Mario Álvarez García, Juan Antonio Fernández Rodríguez, Jorge Yago |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2016 |
Fecha de depósito | 2021-09-21 |
Publicado en |
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ISBN/ISSN | 978-3-319-40113-3 2194-5357 |
Resumen | Stress is one of the most important factors in car accidents. When the
driver is in this mental state, their skills and abilities are reduced. In this paper, we
propose an algorithm to predict stress level on a road. ... Stress is one of the most important factors in car accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to predict stress level on a road. Prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section, the road state (weather and traffic), and the previous driving made by the driver. We employ this algorithm to build a speed assistant. The solution provides an optimum average speed for each road stage that minimizes the stress. Validation experiment has been conducted using five different datasets with 100 samples. The proposal is able to predict the stress level given the average speed by 84.20% on average. The system reduces the heart rate (15.22%) and the aggressiveness of driving. The proposed solution is implemented on Android mobile devices and uses a heart rate chest strap. |
Agencias financiadoras | Ministerio de Economia, Industria y Competitividad (MINECO). España Ministerio de Educación, Cultura y Deporte (MECD). España |
Identificador del proyecto | TIN2013-46801-C4-2-R/1R
PRX15100036 |
Cita | Corcoba Magaña, V., Muñoz Organero, M., Álvarez García, J.A. y Fernández Rodríguez, J.Y. (2016). Estimation of the Optimum Speed to Minimize the Driver Stress Based on the Previous Behavior. En ISAmI 2016: 7th International Symposium on Ambient Intelligence (31-39), Sevilla: Spirnger. |
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