Rule-Based Driving Style Classification Using Acceleration Data Profiles

Driving style classification can be useful for various intelligent vehicle applications and can improve driving comfort in new driveability functions. Within this contribution, we present a rule-based classification algorithm, which uses aggregated speed-depended acceleration data driving profiles from recorded experiments. We observe, that high values in longitudinal and lateral acceleration occur less frequently the greater they are. If a dynamic driving style is present, high values also occur more frequently compared to the average. From that observation we use the occurrence probability of an acceleration data pair as an indicator for driving style. The proposed approach compares the expected average acceleration according to the driving profile with the actual time series data. Based on a sample time series from recorded experiment data, the driving style is then classified into three different classes: calm, moderate and dynamic. Because the approach relies on a limited amount of parameters with low sensitivity, the classification offers high robustness and is not prone to over-fitting. It reaches overall 68.49% accuracy on recorded real life driving data with various driving context.