A Machine Learning Smartphone-based Sensing for Driver Behavior Classification
This addresses driver profiling for insurance and fleet management, but it is incremental as it uses existing methods on simulated data.
The paper tackled driver behavior classification using smartphone sensors by collecting simulated data from Carla Simulator and fusing multi-sensor inputs, achieving evaluation of machine learning algorithms for time series classification.
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.