TY - GEN
T1 - Prototype of Driving Behavior Monitoring System Using Naïve Bayes Classification Method
AU - Pramunanto, Eko
AU - Zaini, Ahmad
AU - Rizkiana, Vathya
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - The transportation needs of the Indonesian people, especially for land routes, are increasing per year. According to Indonesia Central Bureau of Statistics, in 2016 the number of passenger cars in Indonesia was 14,580,666 units. This number rose 7.8% from the previous year. However, the accident rate in Indonesia is still relatively high. In 2017, the number of accidents in Indonesia was 98,400. One reason for the occurrence of traffic accidents is the lack of good driver behavior. In this study, a prototype system was proposed that can monitor the driving behavior of drivers of four-wheeled vehicles using Naïve Bayes Classification method. Using the event detection and feature extraction methods, this system will classify the categories of driver's driving behavior into three classes; defensive, normal, and aggressive. The results of the validation show that the accuracy of the method used is 86% for longitudinal events and 95.8% for lateral events.
AB - The transportation needs of the Indonesian people, especially for land routes, are increasing per year. According to Indonesia Central Bureau of Statistics, in 2016 the number of passenger cars in Indonesia was 14,580,666 units. This number rose 7.8% from the previous year. However, the accident rate in Indonesia is still relatively high. In 2017, the number of accidents in Indonesia was 98,400. One reason for the occurrence of traffic accidents is the lack of good driver behavior. In this study, a prototype system was proposed that can monitor the driving behavior of drivers of four-wheeled vehicles using Naïve Bayes Classification method. Using the event detection and feature extraction methods, this system will classify the categories of driver's driving behavior into three classes; defensive, normal, and aggressive. The results of the validation show that the accuracy of the method used is 86% for longitudinal events and 95.8% for lateral events.
KW - Driving Behavior
KW - Naïve Bayes
KW - event detection
UR - http://www.scopus.com/inward/record.url?scp=85084471076&partnerID=8YFLogxK
U2 - 10.1109/CENIM48368.2019.8973232
DO - 10.1109/CENIM48368.2019.8973232
M3 - Conference contribution
AN - SCOPUS:85084471076
T3 - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
BT - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019
Y2 - 19 November 2019 through 20 November 2019
ER -
