Artificial Intelligence for Smart Electric Vehicle Braking System

Sonki Prasetya†,‡*, Hasvienda.M. Ridlwan‡, Hendri D.S. Budiono†, Ario Sunar Bhaskoro†, Agung Shamsuddin†, Danardono A. Sumarsono†, Mohammad Adhitya†


†Research Center for Advanced Vehicle (RCAVe), Universitas Indonesia, 16424, Indonesia

‡Department of Mechanical Engineering Politeknik Negeri Jakarta 16425, Indonesia.

Corresponding Author Email: sonki.prasetya@pnj.ac.id

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.