A Machine Learning Smartphone-based Sensing for Driver Behavior Classification

Sarra Ben Brahim, Hakim Ghazzai, Hichem Besbes, Yehia Mahmoud Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish (US)
Title of host publication2022 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
ISBN (Print)978-1-6654-8486-2
DOIs
StatePublished - Nov 11 2022

Fingerprint

Dive into the research topics of 'A Machine Learning Smartphone-based Sensing for Driver Behavior Classification'. Together they form a unique fingerprint.

Cite this