Autonomous Vision of Driverless car in Machine Learning

DOI

10.2991/aebmr.k.220405.356How to use a DOI?

Keywords

Driverless car; Machine Learning

Abstract

As one of the main research directions of machine learning and CNN (Convolutional neural network), Autonomous vision and decision-making have many kinds of applications in various fields, especially in Driverless cars and decision-making on highways. In recent years, Tesla, HUAWEI, Google, and Microsoft, these major giants in Driverless cars gradually shifted their attention from traditional cars to driverless cars. Where Google driverless cars are designed to operate safely and autonomously without requiring humans in the city, it used a series of devices, sensors to collect the data from outside. While the Autonomous car in the unstructured terrain also has many applications, like the Defense Advanced Research Projects Agency (DARPA) UGCV-Perceptor Integration (UPI) program was conceived to take a fresh approach to all aspects of autonomous outdoor mobile robot design, where the main aim of UPI program is to make sure the autonomous robot has the ability be transported from Point A to Point B in the limited time in the unstructured terrain and complex outdoor environment. Machine learning occupies a large proportion in this case where it provides robust and adaptive performance, also reduces the cost of development and time. The report will introduce the features, like the detect capability, applicable terrain and different detect-radar to analyze the differences between the two kinds of autonomous cars, and discuss the future applications of autonomous vehicles in unstructured terrain.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

Download article (PDF)

TY  - CONF
AU  - Jiaxuan Lu
PY  - 2022
DA  - 2022/04/29
TI  - Autonomous Vision of Driverless car in Machine Learning
BT  - Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022)
PB  - Atlantis Press
SP  - 2113
EP  - 2117
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220405.356
DO  - 10.2991/aebmr.k.220405.356
ID  - Lu2022
ER  -