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.

Modeling and prediction of driving performance measures based on multi-output convolutional Gaussian process.

Driving performance measures (DPMs) are important indices for driving and personal safety in vehicle operation. The DPMs are collected under various controlled driving conditions to demonstrate different driving behaviors so that mitigating technology interventions can be studied and designed. However, significant costs are involved in the DPM acquisition, and there are a very limited number of controlled driving condition data. Thus, the modeling and prediction of the DPMs under unobserved driving conditions are critical, and many methods have been developed. However, existing literature in this area suffer a common limitation: The interactions among different DPMs are not fully considered (each DPM is modeled individually), although the existence of such interactions is widely reported. This paper proposes a novel DPM modeling and prediction method, i.e., multi-output convolutional Gaussian process (MCGP), that incorporates the interactions among different DPMs. The method features the modeling flexibility for different DPMs and the interpretable modeling structure for integrating the DPM interactions. The method is compared with three benchmark methods on the DPM data set under four different settings, and the results demonstrate the superiorities of the method. Discussions and interpretations of the results are also provided.