2024-03-29T11:24:56Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1358922022-02-07T16:12:18Zcom_10366_133431com_10366_122682com_10366_4666com_10366_3823col_10366_135879
Corcoba Magaña, Víctor
Muñoz Organero, Mario
Álvarez-garcía, Juan Antonio
Fernández Rodríguez, Jorge Yago
2018-01-12T09:27:13Z
2018-01-12T09:27:13Z
2017-09-06
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 6 (2017)
2255-2863
http://hdl.handle.net/10366/135892
Stress is one of the most important factors in traffic accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to estimate the optimal speed to minimize stress levels on upcoming road segments when driving. The prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section to be assessed, the road state (weather and traffic), and the previous drives made by the driver. We use this algorithm to build a speed assistant. The solution provides an optimum average speed for each road segment that minimizes the stress. A validation experiment has been conducted in a real setting using two different types of vehicles. The proposal is able to predict the stress levels given the average speed by 84.20% on average. On the other hand, the speed assistant reduces the stress levels (estimated from the driver’s heart rate signal) and the aggressiveness of driving regardless of the vehicle type. The proposed solution is implemented on Android mobile devices and uses a heart rate chest strap.
application/pdf
eng
Ediciones Universidad de Salamanca (España)
Attribution-NonCommercial-NoDerivs 3.0 Unported
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Computación
Informótica
Computing
Information Technology
Design of a Speed Assistant to Minimize the Driver Stress
info:eu-repo/semantics/article