RT Conference Proceedings
T1 Estimation of the optimum speed to minimize the driver stress based on the previous behavior
A1 Corcoba Magaña, Víctor
A1 Muñoz Organero, Mario
A1 Álvarez-García, Juan Antonio
A1 Fernández-Rodríguez, Jorge Yago
AB 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.
PB Springer Berlin / Heidelberg
SN 978-3-319-40113-3 (print)
SN 978-3-319-40114-0 (online)
SN 2194-5357 (print)
SN 2194-5365 (online)
YR 2016
FD 2016-05-28
LK https://hdl.handle.net/10016/25681
UL https://hdl.handle.net/10016/25681
LA eng
NO Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016),  Seville, Spain on June 1st–3rd
NO The  research  leading  to  these  results  has  received  funding  from  the  “HERMES-SMART DRIVER/CITIZEN” projects TIN2013-46801-C4-2-R /1-R funded by the  Spanish  MINECO,  from  the  grant  PRX15/00036  from  the  Ministerio  de  Educación  Cultura y Deporte.
DS e-Archivo
RD 1 sept. 2024