Simulation of Autonomous Vehicles by Using Machine Learning Methods

Document Type : Scientific - Research

Authors
alireza hoveidafard 1
Sina Fard moradinia 2
Babak Golchin 3
Ali ghaffari 4
1 Ph.D. student in Transportation, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tabriz, Iran
2 Assistant Professor, Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 Assistant Professor, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
4 Associate Professor, Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
10.22119/jte.2023.348840.2609
Abstract
Increasing population and urbanization have been caused many problems such as: increased traffic in cities, travel time, cost, fuel consumption, air pollution and so on. In the mentioned situation, some methods are needed to increase the safety and efficiency of the transportation system. In this regard, one of the programs that can be implemented to improve transportation is the use of Autonomous Vehicles. These vehicles have attracted the attention of researchers and industries, so that many transportation experts are currently working on this field. This system can act as the basis for economic growth and countries development. In the present study, the roles of AVs in transportation system have been discussed. The main structure of this project is neural network as one of the machine learning methods. The task of this section is to learn the system and make decisions based on the current condition.  Accordingly, the combinations of Dense and Convolutional neural networks and Udacity simulator have been used. It showed that the simulator can help to speed up the process and implementation of AVs. The results indicated that there is high similarity between human driving cars and AVs which are implemented by the Udacity simulator.
Keywords
Transportation System
Neural Network
Autonomous Vehicles
Machine Learning
Udacity Simulator
Subjects
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Quarterly Journal of Transportation Engineering
Volume 15, Issue 2 - Serial Number 59
Winter 2024
Pages 3483-3507

  • Receive Date 25 June 2022
  • Revise Date 27 December 2022
  • Accept Date 18 April 2023