Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations of the current ADAS and their possible solutions. This study 1) investigated the limitations and solutions of ADAS through a literature review, 2) identified the causes and effects of ADAS through consumer complaints using natural language processing models, and 3) compared the major differences between the two. These two lines of research identified similar categories of ADAS causes, including human factors, environmental factors, and vehicle factors. However, academic research focused more on human factors of ADAS issues and proposed advanced algorithms to mitigate such issues while drivers complained more of vehicle factors of ADAS failures, which led to associated top consequences. The findings from these two sources tend to complement each other and provide important implications for the improvement of ADAS in the future.

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Advanced driving assistance systems (ADAS) are designed to reduce potential crash risks and enhance driving safety. However, drivers’ interactions with ADAS may vary depending on their individual driving styles and characteristics. This study proposes a novel approach to classifying driving styles and explores how age and gender affect interactions with ADAS. The study utilized two naturalistic driving data sets comprising 148 drivers from four age groups: teens; younger adults; middle-aged adults; and older adults. Data were collected during two periods: baseline (without ADAS); and treatment (with ADAS). First, the K-means clustering algorithm was employed to divide trips into one conservative and two aggressive groups based on three driving behavior metrics: tailgating; speeding; and lane-changing. The aggressive-trip ratios were then calculated for each driver during each of the two periods. The Bayesian Gaussian mixture model was applied to determine the threshold values of the aggressive-trip ratios to classify drivers as conservative, moderate, or aggressive during each period. This allowed for identifying changes in driving style upon the activation of ADAS. The subsequent multinomial logistic regression model results showed that driving styles vary across age groups, with teens being the most aggressive drivers. Certain changes in driving style were observed, with some conservative drivers becoming aggressive or moderate and some aggressive drivers becoming conservative or moderate, but these differences were statistically non-significant. The findings of this study indicate that warning-based ADAS may not elicit significant changes in driving style, particularly among teenage drivers who are consistently the most aggressive drivers.