A Bayesian Tobit quantile regression approach for naturalistic longitudinal driving capability assessment. (November 2020) - Yu, Rongjie; Long, Xiaojie; Quddus, Mohammed; Wang, Junhua
Highlights: Proposed two Responsibility-Sensitive Safety (RSS) based longitudinal driving capability indicators. Utilized Bayesian Tobit quantile regression (BTQR) models to quantify driving capability with trip level characteristics Presented case studies for longitudinal driving capability assessment. Discussed model applications for fleet safety management and autonomous vehicle (AV) performance evaluations. Abstract: Given the severe traffic safety issue, tremendous efforts have been devoted to identify the crash contributing factors for developing and implementing safety improvement countermeasures. According to the study findings, driving behaviors have attributed to the majority crash occurrence, among which inadequate driving capability is a key factor. Therefore, a number of studies have been conducted for developing techniques associated with the driving capability assessment and its various improvement. However, the conventional assessment approaches, such as driving license exams and vehicle insurance quotes, have only focused on basic driving skill evaluations or aggregated driving style classifications, which failed to quantify driving capability from the safety perspective with respect to the complex driving scenarios. In this study, a novel longitudinal driving capacity assessment and ranking approach was developed with naturalistic driving data. Two Responsibility-Sensitive Safety (RSS) based driving capability indicators from the perspectives of risk exposure and severity were first proposed. Then, Bayesian Tobit quantile regression (BTQR) models were introduced to explore the relationships between driving capability indicators with trip level characteristics from the aspects of travel features, operational conditions, and roadway characteristics. The modeling results concluded that nighttime driving and higher average speed would lead to higher longitudinal collision risk and its severity. Besides, the BTQR models have provided varying factors significances among different quantile levels, for instance, driving duration is only significant at high quantiles for the driving capability indicators, implying that duration only affects drivers with large longitudinal risk exposures and strong close following tendencies. Furthermore, the case studies provided how to deploy the developed model to obtain the relative longitudinal driving capability rankings. Finally, the model applications from the aspects of commercial fleet safety management and comparing the autonomous vehicles' longitudinal driving behaviors with human drivers have been discussed.