Developing Autonomous Vehicle Systems Using Machine Learning Techniques and Comparison of SVC and Naive Bayes Algorithms
D.Vineela Chandra1, J. K. R Sastry2
1D Vineela Chandra, Department of ECM, KL Deemed to Be University, Vijayawada, (Andhra Pradesh), India.
2Dr. J K R Sastry, Department of ECM, KL Deemed to Be University, Vijayawada, (Andhra Pradesh), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1622-1626 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6125058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Autonomous vehicles are very famous now a days. To increase the comfort and to save time, Machine learning concepts are used to achieve autonomous driving. We initially train the vehicle manually through remote access using internet. During this training, we obtain data from sensors regarding object distance around the vehicle at every instance of time and current direction of the vehicle. Later we feed this data into machine learning algorithms and develop a classifier which predicts the directions for new sensor data using previous experience. In this paper, the effect of different algorithms on the vehicle and accuracy comparisons between those algorithms is presented.
Keyword: Autonomous Vehicles, Machine Learning, Ultra Sonar, Object Distance.
Scope of the Article: Machine Learning.