Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment. (June 2021) - Khattak, Zulqarnain H.; Fontaine, Michael D.; Li, Wan; Khattak, Asad J.; Karnowski, Thomas
Highlights: How are instantaneous driving decisions expressed through volatility related to involvement in safety critical events? New measures of volatility based on Bollinger bands are introduced. Volatility represented by Bollinger bands in both lateral and longitudinal direction increases the risk of involvement in unsafe outcome. Model based on 20 s of time series prior to involvement in unsafe outcomes show high prediction accuracy of 88.1 % and 85.1 %. Abstract: The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively. The research demonstrates how measures and analysis of driving volatility can be leading indicators of crashes and contribute to enhancing safety. Highly detailed microscopic data from naturalistic driving are used to provide the analytic framework to rigorously analyze the behavioral dimensions and driving instability that can lead to different types of SCEs such as roadway departures, rear end collisions, and sideswipes. Modeling results reveal a positive association between volatility and involvement in SCEs. Specifically, increases in both lateral and longitudinal volatilities represented by Bollinger bands and vehicular jerk lead to higher likelihoods of involvement in SCEs. Further, driver behavior related factors such as aggressive driving and lane changing also increases the likelihood of involvement in SCEs. Driver distraction, as represented by the duration of secondary tasks, also increases the risk of SCEs. Likewise, traffic flow parameters play a critical role in safety risk. The risk of involvement in SCEs decreases under free flow traffic conditions and increases under unstable traffic flow. Further, the model shows prediction accuracy of 88.1 % and 85.7 % for training and validation data. These results have implications for proactive safety and providing in-vehicle warnings and alerts to prevent the occurrence of such SCEs.