Estimation of the Optimum Speed to Minimize the Driver Stress Based on the Previous Behavior

  • Victor Corcoba Magaña
  • Mario Muñoz Organero
  • Juan Antonio Álvarez-García
  • Jorge Yago Fernández Rodríguez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 476)

Abstract

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.

Keywords

Intelligent transport system Stress driver Driving assistant Deep learning Particle Swarm Optimization Android Mobile computing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Victor Corcoba Magaña
    • 1
  • Mario Muñoz Organero
    • 1
  • Juan Antonio Álvarez-García
    • 2
  • Jorge Yago Fernández Rodríguez
    • 2
  1. 1.Telematic Engineering DepartmentUniversidad Carlos III de MadridLeganésSpain
  2. 2.Computer Languages and Systems DepartmentUniversity of SevilleSevilleSpain

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