Comparing Algorithms for Aggressive Driving Event Detection Based on Vehicle Motion Data

Aggressive driving is one of the main causes of fatal crashes. Correctly identifying aggressive driving events still represents a challenge in the literature. Furthermore, datasets available for testing the proposed approaches have some limitations since they generally (a) include only a few types of events, (b) contain data collected with only one device, and (c) are generated in drives that did not fully consider the variety of road characteristics and/or driving conditions. The main objective of this work is to compare the performance of several state-of-the-art algorithms for aggressive driving event detection (belonging to anomaly detection-, threshold- and machine learning-based categories) on multiple datasets containing sensors data collected with different devices (black-boxes and smartphones), on different vehicles and in different locations. A secondary objective is to verify whether smartphones could replace black-boxes in aggressive/non-aggressive classification tasks. To this aim, we propose the AD$^2$ (Aggressive Driving Detection) dataset, which contains (i) data collected using multiple devices to evaluate their influence on the algorithm performance, (ii) geographical data useful to analyze the context in which the events occurred, (iii) events recorded in different situations, and (iv) events generated by traveling the same path with aggressive and non-aggressive driving styles, in order to possibly separate the effects of driving style from those of road characteristics. Our experimental results highlighted the superiority of machine learning-based approaches and underlined the ability of smartphones to ensure a level of performance similar to that of black-boxes.