TY - GEN
T1 - A Machine Learning Smartphone-based Sensing for Driver Behavior Classification
AU - Brahim, Sarra Ben
AU - Ghazzai, Hakim
AU - Besbes, Hichem
AU - Massoud, Yehia Mahmoud
N1 - KAUST Repository Item: Exported on 2022-11-15
PY - 2022/11/11
Y1 - 2022/11/11
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/685677
UR - https://ieeexplore.ieee.org/document/9937801/
U2 - 10.1109/ISCAS48785.2022.9937801
DO - 10.1109/ISCAS48785.2022.9937801
M3 - Conference contribution
SN - 978-1-6654-8486-2
BT - 2022 IEEE International Symposium on Circuits and Systems (ISCAS)
PB - IEEE
ER -