Highlights Drivers’ behavior varies under different traffic and environmental conditions. Extreme drivers’ volatility is highly correlated with traffic critical safety events. Characterizing Kinematic-based Surrogate Measure of Safety (K-SMoS) on Basic Safety Messages could measure drivers’ behavior in Connected Vehicle (CV) environments. Extreme Value Theorem incorporates proper statistical techniques for exploring unusual extreme behaviors. Extreme value analysis of K-SMoS could spatiotemporally detect traffic crashes in CV environments.

    Abstract Connected Vehicles (CVs) technology has provided large-scale driving database embedded in Basic Safety Messages (BSMs). This valuable data source can shed more light on tracking individual driving behaviors to detect crashes. This study delves into the possibility of detecting crashes using BSMs in controlled high-fidelity driving simulator experiments. To this end, two driving simulator scenarios were designed to simulate Run-off-Road (ROR), and Rear-End (RE) crashes. Twenty-four professional truck drivers were recruited to drive the scenarios. In each scenario, crash and non-crash cases were identified from vehicles’ trajectories, resulting in four study cases. Drivers’ behaviors were quantified by characterizing two Kinematic-based Surrogate Measures of Safety (K-SMoS), namely Absolute value of Derivative of Instantaneous Acceleration (ADInstAccel) and Absolute value of Derivative of Steering (ADSteering). Extreme defensive driving volatilities under crash and non-crash cases were modeled by extreme value analysis of K-SMoS and fitting their associated Generalized Extreme Value (GEV) distributions under Bayesian inference. Accordingly, for each K-SMoS, the crash detection was formulated as a binary classification between two K-SMoS GEV continuous distributions under crash and corresponding non-crash conditions. Qualitative uncertainty analysis of joint posterior density distributions of GEVs’ parameters revealed a higher uncertainty of extreme driving behaviors in crash conditions. Regardless, notable relative increases in the central tendency of extreme K-SMoS in crash compared to non-crash conditions were found, implying the possibility of crash detection by tracking extreme drivers’ behaviors using trajectory-level observations. This visual inference was affirmed by the result of binary classification of GEV distributions associated with K-SMoS. Depending on the crash type and K-SMoS, 71% to 81% accuracy in crash detection was obtained, where ADSteering outperformed ADInstAccel in terms of the discriminative ability. Besides, using sensitivity–specificity analysis, the optimal threshold of 1.24 (rad/s) and 1.31 (m/s3), respectively, for ADSteering and ADInstAccel, were identified to detect crashes. These findings can potentially enhance CVs' automation level in spatiotemporally identifying crash-prone conditions to disseminate distress notifications. Furthermore, the introduced methodology can be a complementary one to what has been followed in the crash detection domain.


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    Titel :

    Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment


    Beteiligte:


    Erscheinungsdatum :

    2021-12-21




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch