End-to-End Learning-based Self-Driving Control Imitating Human Driving

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3
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4

초록

In recent years, End-to-End learning-based self-driving cars have been actively researched. Unlike conventional methods, neural networks are trained to drive like human drivers by mapping directly from sensory data to control commands. In this paper, we propose a neural network architecture for recognizing visual information and controlling the steering and speed of the vehicle like humans.

키워드

End-to-End learning; Autonomous driving
제목
End-to-End Learning-based Self-Driving Control Imitating Human Driving
저자
Kim, Donghyun; Kwon, Jaerock; Nam, Haewoon
DOI
10.1109/ICTC52510.2021.9620894
발행일
2021-10
유형
Proceedings Paper
저널명
12TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC 2021): BEYOND THE PANDEMIC ERA WITH ICT CONVERGENCE INNOVATION
권
2021-October
페이지
1763 ~ 1765