Abstract
In this article we present the detection of fatigue level of a vehicle driver and according to this level the autonomous driving assistance is implemented, the detection of fatigue level is based on facial recognition using deep learning (Deep Learning) developed in the Matlab software, applying neural networks previously trained and designed, this detection sends us a metric that comprises four levels, according to the metric the position and velocity control of a simulated Car-Like vehicle in the Unity3D software is performed which presents a user friendly environment with the use of haptic devices, the development of the control algorithm is based on path correction which calculates the shortest distance to reenter the desired path.
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Acknowledgements
The authors would like to thanks to the Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia –CEDIA for the financing given to research, development, and innovation, through the CEPRA projects, especially the project CEPRA-XI-2017- 06; Control Coordinado Multi-operador aplicado a un robot Manipulador Aéreo; also to Universidad de las Fuerzas Armadas ESPE, Universidad Técnica de Ambato, Escuela Superior Politécnica de Chimborazo, and Universidad Nacional de Chimborazo, and Grupo de Investigación en Automatización, Robótica y Sistemas Inteligentes, GI-ARSI, for the support to develop this work.
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Mayorga, O.A., Andaluz, V.H. (2018). Autonomous Control Through the Level of Fatigue Applied to the Control of Autonomous Vehicles. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_12
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DOI: https://doi.org/10.1007/978-3-030-05204-1_12
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