Identification of aggressive driving from naturalistic data in car-following situations
Journal article, 2020

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.

Naturalistic driving

Self-reported questionnaires

Jerk metrics

Aggressive driving



Jordanka Kovaceva

Chalmers, Mechanics and Maritime Sciences, Vehicle Safety

Irene Isaksson-Hellman

If Insurance

Nikolce Murgovski

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Journal of Safety Research

0022-4375 (ISSN)

Vol. In Press

Subject Categories

Infrastructure Engineering

Applied Psychology

Vehicle Engineering



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