Virtual Self Driving Car using Improved Convolution Neural Networks

被引：1

作者：

Jinila, Y. Bevish ^{[1
]}

Jabez, J. ^{[1
]}

Shyry, S. Prayla ^{[1
]}

机构：

[1] Sathyabama Inst Sci & Technol, Sch Comp, Chennai, Tamil Nadu, India

来源：

2021 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMMUNICATION, COMPUTER TECHNOLOGIES AND OPTIMIZATION TECHNIQUES (ICEECCOT) | 2021年

关键词：

CNN; Deep Learning; Self-driving car; lane detection; PilotNet; Computer Vision;

D O I：

10.1109/ICEECCOT52851.2021.9708035

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

recently, the technological advancements have led to the adoption of technology in the automobile sector. There has been a constant improvement in the design and development of self-driving cars. The primary goal of self-driving car is to make the vehicle move in the correct lane. So, lane detection is one of the preliminary steps to make this possible. Existing literatures have suggested various schemes and methods to implement and improve the efficiency of the self-driving cars. In this paper, improved CNN model has been used to develop the self-driving car. This scheme outperforms by with less hardware resource, processor power and memory size.

引用

页码：215 / 219

页数：5

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