Quantifying and recommending seat belt reminder timing using naturalistic driving video data.

INTRODUCTION

To better understand the timing of when people buckle their seat belt, an analysis of a naturalistic driving study was used. The study provided a unique perspective inside of the vehicle where the entire seat belt was visible from the time the driver entered the vehicle to one minute of driving forward or 32 kph.

METHOD

Seat belt buckling behavior was identified for 30 drivers. An additional 10 drives for 13 of these drivers were identified for a seat belt sequencing, which identified the points when the vehicle was put into ignition, shifted, when vehicle movement began, and when the seat belt was buckled. The speed at belt closure was also identified. The timing from ignition to buckle and to shifting into forward gear were examined to identify the speed and appropriate timing for seat belt reminders.

RESULTS

The data show that drivers were buckled in over 92% of the 3,102 drives. In addition, in 70% of those total drives, the drivers were buckled before the vehicle began movement. Of greater interest for seat belt reminders/interlocks are those drives when drivers buckle after movement. When considering time from ignition to seat belt closure, the mean was 27.5 s. Because higher speeds are typically reached when traveling forward rather than reverse, it was important to know the time duration from shifting into drive to buckling. With this consideration, the mean to buckle dropped to 16.2 s. The mean speed at buckling when traveling forward was 15.3 kph. From the regression analysis, the input variables 'Age,' 'Sex,' 'Weight,' 'Environment,' and 'Weather' are significant contributors in predicting the log odds of a driver putting on seatbelt.

CONCLUSIONS

With the understanding that higher speeds lead to an increased risk of injury and/or death and with the results of the analysis, a recommendation of a 30 s time from forward shift and a 25 kph (6.9 m/s) threshold for reminder systems should be implemented. The regression analysis also validates that most of the predicted seat belt buckling times are within 30 s. Practical Applications: This would reduce perception of nuisance alerts and protect the driver from higher speed unbuckled crashes. The seat belt buckling time prediction model also demonstrates good potential for developing tailored buckling warning system for different drivers.