Affordable Access

deepdyve-link
Publisher Website

Vision-Based Obstacle Detection and Collision Prevention in Self-Driving Cars

Authors
  • Sarada Devi, Y
  • Sathvik, S
  • Ananya, P
  • Tharuni, P
  • Naga Krishna Vamsi, N
Type
Published Article
Journal
Journal of Physics Conference Series
Publisher
IOP Publishing
Publication Date
Sep 01, 2022
Volume
2335
Issue
1
Identifiers
DOI: 10.1088/1742-6596/2335/1/012019
Source
ioppublishing
Keywords
Disciplines
  • Paper
License
Unknown

Abstract

With increasing computational power and a vast amount of data to work with, deep learning has risen to prominence since the 2010s. Numerous applications are being researched and developed using deep learning. One of the applications is computer vision in self-driving cars. Convolutional Neural Networks (CNNs) are being widely used because of their high performance compared to other alternative techniques in several perception and control tasks. The Convolutional Neural Networks (CNNs) allows the automobile to learn from different types of roads, scenarios allowing the car to forecast its route on any particular road with minimum inaccuracy. This paper proposes a working model of the autonomous car, which has a Raspberry Pi 4 Model B as the control unit and processing unit. This working model gets real-time images from the Raspberry Pi camera and these images are used by the CNN model, which predicts the direction the car must turn. The raspberry pi sends the control signals to the L298n motor driver. The trained CNN model achieved an accuracy of 96.39% with the test dataset and 84.77% with the validation dataset.

Report this publication

Statistics

Seen <100 times