End-to-End Learning Model Design for Steering Autonomous Vehicle

被引：0

作者：

Karsli, Munir ^{[1
]}

Satilmis, Yusuf ^{[1
]}

Sara, Muhammed ^{[1
]}

Tufan, Furkan ^{[1
]}

Eken, Suleyman ^{[1
]}

Sayar, Ahmet ^{[1
]}

机构：

[1] Kocaeli Univ, Bilgisayar Muhendisligi, Umuttepe Kampusu, TR-41380 Izmit, Turkey

来源：

2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2018年

关键词：

autonomous vehicles; end-to-end deep learning; intelligent transportation systems; racecar mini autonomous car;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Safe autonomous vehicle steering is one of the most difficult aspects of an artificial intelligence system. Therefore, researchers have concentrated on training deep network models from front-facing camera data synchronized with the steering angles. In this study, three different end-to-end deep learning models are developed and the success of these models on the Racecar mini autonomous vehicle is evaluated. Experimental analyzes show that proposed models give successful results in steering the vehicle autonomously. The proposed models can extensible so that information such as angle, speed, and brake can be acquired instantaneously from the road view taken from the autonomous vehicle.

引用

页数：4

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