Predictions of Undesirable behaviors while driving using Support Vector Machine
Conference proceedings article
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Author list: Nattawut Phramorathat, Piyasawat Navaratana Na Ayudhya, Tirasak Sapaklom, Ekkachai Mujjalinvimut and Jakkrit Kunthong
Publication year: 2023
URL: https://ieeexplore.ieee.org/document/10296763/
Abstract
Automobile vehicles today are not only used for transportation or solely for driving to the desired destination, but can also be used for leisure. Distraction driving often result in can results in driver distractions causing road accidents. According to research studies for detecting driver distractions, which discovered weaknesses in both the comfort and privacy of drivers. In this research, full and half bridge loadcells are installed on the seat and backrest, total of 9 points to detect weight changes when changing the posture of driver. The sensor signals are analyzed using Support Vector Machine (SVM), which is Machine Learning deployed to identify 13 different poses. Measurement data taken from 20 people were analyzed with Support Vector Machine, which revealed the highest prediction certainty of 99.89 percent
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