Sleepiness Detection for Cooperative Vehicle Navigation Strategies

Abstract

Human-robot interaction (HRI) techniques has become important in the resolution of problems that can not be automated completely. Moreover, the design of strategies based on such interactions can help to compensate the agents respective limitations. This work aims the development of a HRI system, which is based on driver sleepiness to improve safety in a Renault Twizy vehicle. For this purpose, eye behavior measurements are extracted using a properly machine learning algorithm that can operate under varying illumination levels. Perclos and blink frequency measures are considered to estimate diver sleepiness, and based in such parameters, the vehicle speed is limited, which may help decrease accidents rate. The presented architecture can improve the security conditions of navigation for both users and pedestrians. The results show that it is possible to implement HRI strategies based on cognitive factors in urban areas to prevent fatal accidents.