A Machine Learning Smartphone-based Sensing for Driver Behavior Classification
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BenBrahim_FinalVersion_ISCAS22.pdf
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Accepted Manuscript
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Conference PaperKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionElectrical and Computer Engineering Program
Innovative Technologies Laboratories (ITL)
Date
2022-11-11Permanent link to this record
http://hdl.handle.net/10754/685677
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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.Citation
Brahim, S. B., Ghazzai, H., Besbes, H., & Massoud, Y. (2022). A Machine Learning Smartphone-based Sensing for Driver Behavior Classification. 2022 IEEE International Symposium on Circuits and Systems (ISCAS). https://doi.org/10.1109/iscas48785.2022.9937801Publisher
IEEEConference/Event name
2022 IEEE International Symposium on Circuits and Systems (ISCAS)ISBN
978-1-6654-8486-2arXiv
2202.01893Additional Links
https://ieeexplore.ieee.org/document/9937801/https://ieeexplore.ieee.org/document/9937801/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9937801
ae974a485f413a2113503eed53cd6c53
10.1109/ISCAS48785.2022.9937801