A method for identifying aggressive driving by using naturalistic driving data

Paper i proceeding, 2018

Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. By understanding the behavior of the drivers and finding quantitative ways to categorize the behavior associated with higher crash risk, programs for modifying driver behavior towards safer driving can be designed. The objective of this study is to identify aggressive drivers by metrics calculated from naturalistic driving data.
The drivers are separated by the aggressive behavior of following too closely to a front vehicle, i.e. tailgating. Furthermore, two jerk metrics are calculated to identify aggressive drivers: a) number of large positive jerks when pressing the gas pedal and b) number of large negative jerks when pressing the brake pedal. Moreover, drivers’ gender, Arnett Inventory of Sensation Seeking (AISS) score, Driver Behavior Questionnaires (DBQ) and country effects on the metrics are analyzed.
The results show that the aggressive drivers, defined for car following situations using tailgating metric, were associated with significantly higher frequency of using large negative jerk. The results could be potentially applied in programs for driver training and education, advanced driver coaching, and in the context of usage-based insurance.