Machine Learning Techniques to Identify Unsafe Driving Behavior by Means of In-Vehicle Sensor Data

Traffic crashes are one of the biggest causes of accidental death in the way where, every year, more than 1.35 million of people die. In most of them, the main cause is related to the driver’s behavior. The driver performs a set of actions on the vehicle commands, such as steering, braking, accelerating or changing gear, which generate a direct response of the vehicle, or other tasks, such as visual, auditory, or haptic related tasks (e.g. looking for items, listening to radio, and using a smartphone), which can still impact on the driving safety. In this work we propose a methodology based on machine learning techniques aimed at recognizing safe and unsafe driving behaviors by means of in-vehicle sensor data. Starting from these signals we compute a set of descriptive features capable to accurately describe the behavior of the driver. Two different classification tools, namely Support Vector Machines and feed-forward neural networks, have been trained and tested on a publicly available dataset containing more than 26 hours of total driving time. The classification results report an average accuracy above 90% for both classifiers and the McNemar test shows no performance difference between the models at the 0.05 significance level, demonstrating a concrete possibility of identifying unsafe driving using in-vehicle sensor data.