Identification of Out-of-the-Normal Driving Behaviors Using Instantaneous Driving Decisions—A Case-Study on Indian Drivers

Understanding the indigenous driving styles of individuals is significant to avoid oversighting the behavioral generalization across varying geographical locations. The past research on driving style classification mostly focused on identifying the driving patterns using the kinematic feature magnitudes. The variation in the instantaneous driving decisions termed as “driving volatility” is not explored in the context of performance assessment. In this regard, the present study proposes a methodology to explore the driving styles of Indian drivers, using both the magnitude and variation exhibited in the short-term driving decisions. The real-time driving profiles of 47 professional car drivers were collected and segmented into maneuvers based on the respective driving regimes. The performance features representative of each maneuver are extracted, defining 12 measures of driving volatility. The K-means clustering was performed on the event dataset at two-levels, which resulted in four patterns of driving styles under acceleration and braking regimes. The results showed that, a driver can exhibit speedy and aggressive maneuvers in a stable as well as in a highly volatile pattern. The generated driving style profiles at individual-level highlight the behavioral changes in drivers pertained to external influencing factors, and helps to identify the aberrations performed in each trip.