This paper proposes a multi-modal sensor fusion algorithm for the estimation of driver drowsiness. Driver sleepiness is believed to be responsible for more than 30% of passenger car accidents and for 4% of all accident fatalities. In commercial vehicles, drowsiness is blamed for 58% of single truck accidents and 31% of commercial truck driver fatalities. This work proposes an innovative automatic sleep-onset detection system. Using multiple sensors, the driver’s body is studied as a mechanical structure of springs and dampeners. The sleep-detection system consists of highly sensitive triple-axial accelerometers to monitor the driver’s upper body in 3-D. The subject is modeled as a linear time-variant (LTV) system. An LMS adaptive filter estimation algorithm generates the transfer function (i.e. weight coefficients) for this LTV system. Separate coefficients are generated for the awake and asleep states of the subject. These coefficients are then used to train a neural network. Once trained, the neural network classifies the condition of the driver as either awake or asleep. The system has been tested on a total of 8 subjects. The tests were conducted on sleep-deprived individuals for the sleep state and on fully awake individuals for the awake state. When trained and tested on the same subject, the system detected sleep and awake states of the driver with a success rate of 95%. When the system was trained on three subjects and then retested on a fourth “unseen” subject, the classification rate dropped to 90%. Furthermore, it was attempted to correlate driver posture and sleepiness by observing how car vibrations propagate through a person’s body. Eight additional subjects were studied for this purpose. The results obtained in this experiment proved inconclusive which was attributed to significant differences in the individual habitual postures.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver drowsiness detection using multimodal sensor fusion


    Contributors:

    Conference:

    Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004 ; 2004 ; Orlando,Florida,United States


    Published in:

    Publication date :

    2004-04-12





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Driver Drowsiness Detection

    Satish, K. / Lalitesh, A. / Bhargavi, K. et al. | IEEE | 2020


    Driver drowsiness detection

    YANG HSIN-HSIANG / PRAKAH-ASANTE KWAKU O | European Patent Office | 2015

    Free access

    Driver Drowsiness Detection

    Rezaei, Mahdi / Klette, Reinhard | Springer Verlag | 2017


    DRIVER DROWSINESS DETECTION

    YANG HSIN-HSIANG / PRAKAH-ASANTE KWAKU O | European Patent Office | 2015

    Free access

    Multi-sensor driver drowsiness monitoring

    Boyraz,P. / Acar,M. / Kerr,D. et al. | Automotive engineering | 2008