Road traffic accidents are a growing public health problem. Tracking the driving behavior in real-time and providing feedback on how a driver is behaving on the road is proposed to reduce risky behaviors.
A recent study published on arXiv.org proposes to classify the driving behavior into four different classes using data collected from smartphones’ sensors only.
The data for training and testing is collected in the form of time series by simulating the driving environment on the Carla simulator. Data from sensors such as accelerometer and gyroscope are used. Every trajectory is also enriched with weather information and road data provided by environmental sensors. Time series data are then analyzed to train an AI-based classifier.
Results show the ability to achieve accuracy greater than 88% in detecting driving profiles for a one-minute duration trip.
Driver behavior profiling is one of the main issues in the insurance industries and fleet management, thus being able to classify the driver behavior with low-cost mobile applications remains in the spotlight of autonomous driving. However, using mobile sensors may face the challenge of security, privacy, and trust issues. To overcome those challenges, we propose to collect data sensors using Carla Simulator available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) taking into account the speed limit of the current road and weather conditions to better identify the risky behavior. Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance.
Research paper: Ben Brahim, S., Ghazzai, H., Besbes, H., and Massoud, Y., “A Machine Learning Smartphone-based Sensing for Driver Behavior Classification”, 2022, Link: https://arxiv.org/abs/2202.01893