TY - JOUR
T1 - Artificial intelligence for smart electric vehicle braking system
AU - Prasetya, Sonki
AU - Ridlwan, Hasvienda M.
AU - Budiono, Hendri D.S.
AU - Bhaskoro, Ario Sunar
AU - Shamsuddin, Agung
AU - Sumarsono, Danardono A.
AU - Adhitya, Mohammad
N1 - Publisher Copyright:
© 2020 Zibeline International Publishing Sdn. Bhd.. All rights reserved.
PY - 2020
Y1 - 2020
N2 - A braking system is an essential value in a vehicle particularly for a safety precaution. The higher number rate of traffic accidents mostly in Indonesia shows that the dominant cause of accidents is due to the human factor. The physical condition such as tired or sleepy during driving is the primary problem according to the survey. In order to assist a person when driving a vehicle, an artificial intelligence method is necessary to be integrated to ensure the safety of surrounding people inside and outside the vehicle. This study focuses on implementing the method of identifying object and distance to provide an indicator for braking action particularly for Electric Vehicle (EV). Images from a stereo camera is processed by machine learning via a neural network technique using a mini computer to classify as well as the distance of objects. Furthermore, selections of priority are done to obtain the intensity of braking action. The result shows that process of classification and measurement requires period around 200 ms. Furthermore, braking action done by fuzzy controller sub-system shows that the intensity has smoother signal with the object distance variation compare to the direct method. The methodology done by firstly identify the presence of the object via a stereo camera, later on the decision of braking intensity is generated by two processing unit namely conventional and fuzzy unit. This is achieved by processing the data saved from the object detection using two system via Matlab software. The result of object identification shows distance measurement accuracy around 97% meanwhile the period of object detection is 215 ms. Moreover, the response of braking intensity using data is processed with both conventional and fuzzy unit systems are also presented. The latter method of braking intensity response indicates subtle dynamics. The application of this study is for the heavy vehicle such buses or trucks that requires higher safety during the journey.
AB - A braking system is an essential value in a vehicle particularly for a safety precaution. The higher number rate of traffic accidents mostly in Indonesia shows that the dominant cause of accidents is due to the human factor. The physical condition such as tired or sleepy during driving is the primary problem according to the survey. In order to assist a person when driving a vehicle, an artificial intelligence method is necessary to be integrated to ensure the safety of surrounding people inside and outside the vehicle. This study focuses on implementing the method of identifying object and distance to provide an indicator for braking action particularly for Electric Vehicle (EV). Images from a stereo camera is processed by machine learning via a neural network technique using a mini computer to classify as well as the distance of objects. Furthermore, selections of priority are done to obtain the intensity of braking action. The result shows that process of classification and measurement requires period around 200 ms. Furthermore, braking action done by fuzzy controller sub-system shows that the intensity has smoother signal with the object distance variation compare to the direct method. The methodology done by firstly identify the presence of the object via a stereo camera, later on the decision of braking intensity is generated by two processing unit namely conventional and fuzzy unit. This is achieved by processing the data saved from the object detection using two system via Matlab software. The result of object identification shows distance measurement accuracy around 97% meanwhile the period of object detection is 215 ms. Moreover, the response of braking intensity using data is processed with both conventional and fuzzy unit systems are also presented. The latter method of braking intensity response indicates subtle dynamics. The application of this study is for the heavy vehicle such buses or trucks that requires higher safety during the journey.
KW - Braking
KW - Electric Vehicle
KW - Fuzzy
KW - Machine Learning
KW - Stereo Camera
UR - http://www.scopus.com/inward/record.url?scp=85091466135&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85091466135
SN - 1024-1752
VL - 43
SP - 106
EP - 112
JO - Journal of Mechanical Engineering Research and Developments
JF - Journal of Mechanical Engineering Research and Developments
IS - 6
ER -