Development of a neuro-fuzzy integrating multi-tendency indices system for countering driver fatigue

Abstract

In this study a neuro-fuzzy integrating system, a possible more economical solution for detecting and combating driver fatigue, was developed. First, we surveyed and selected adaptive tendency indices (i.e. Relative Strength Index (RSI), Stochastic Oscillators (KDL) and Moving Average Convergence Divergence (MACD)) based actual driving performance to predict driver fatigue and found that RSI, KDL and MACD of vehicle speed indicated significantly the different momentum before and after fatigue occurring, RSI and MACD were also significant in lateral position, but not significant in steering wheel angle. Second, a neuro-fuzzy technology was used to integrate multi-tendency indices for exactly predicting fatigue and control alarm for combating fatigue. The experiment results showed that the neuro-fuzzy system could efficiently alert subjects for keeping concentration on traffic conditions. The alarm reducing some symptoms of fatigue is marginally significant.