Effects of In-vehicle Information on Driver Blink Characteristics and Workload

A real-vehicle experiment was carried out to study the effects of in-vehicle information on driver workload, during which data of the driver blink duration and frequency were collected to check for discrepancies among drivers with and without vehicle navigation usage. In the meanwhile, the blink characteristics of drivers with vehicle navigation device mounted at three different positions were explored through image prompt or image & sound multi-channel simultaneous prompt. Experimental results showed that when the data of blink with a duration of 0-200ms was distributed at a 10ms interval, the driver blink count distribution curve shows obvious bimodal characteristics. The peak of blink with 50-60ms duration was lower than that with 10-20ms duration when vehicle navigation was not used, and higher when vehicle navigation was used. The difference between medium and long blinks was not significant when the navigation device was mounted at different positions, yet the short blinks showed a significant difference. The peaks of blink count without voice navigation were all greater than those with voice navigation. In particular, without voice navigation, the short blinks increased obviously, and the medium blinks increased relatively, but the long blinks remained almost unchanged. The above results indicate that the driver workload was greater when using vehicle navigation. When the navigation device is installed in position B, the driver workload reaches the minimum. Using voice navigation could reduce driver workload.

[1]  Simone Benedetto,et al.  Driver workload and eye blink duration , 2011 .

[2]  P A Hancock,et al.  The distraction effects of phone use during a crucial driving maneuver. , 2003, Accident; analysis and prevention.

[3]  Mike McDonald,et al.  Are drivers aware of their behavior changes when using In-Vehicle systems , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[4]  F. Thomas Eggemeier,et al.  Workload assessment methodology. , 1986 .

[5]  Yingchun Chen,et al.  The eye activity measurement of mental workload based on basic flight task , 2012, IEEE 10th International Conference on Industrial Informatics.

[6]  Jing Zhang,et al.  Driver cognitive workload estimation: a data-driven perspective , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[7]  Rosaldo J. F. Rossetti,et al.  IC-DEEP: A serious games based application to assess the ergonomics of in-vehicle information systems , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[8]  Gavriel Salvendy,et al.  Handbook of Human Factors and Ergonomics , 2005 .