Open AccessIntelligent Vehicle Design based on PaddlePaddle and Deep Learning
Author(s) -
Song He,
Hao Xue,
Liang Guo,
Xin Chen,
Jun Hu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2132/1/012003
Subject(s) - deep learning , computer science , frame (networking) , artificial intelligence , scheme (mathematics) , field (mathematics) , intelligent transportation system , engineering , mathematical analysis , telecommunications , civil engineering , mathematics , pure mathematics
.In order to visualize the applications of deep learning based intelligent vehicle in the real field vividly, especially in the unmanned cases in which it realizes the integration of various technologies such as automatic data acquisition, data model construction, automatic curve detection, traffic signs recognition, verification of the unmanned driving, etc. A M-typed Model intelligent vehicle that is embedded with a high-performance board from Baidu named Edge Board is adopted by this study. The vehicle is trained under the PaddlePaddle deep learning frame and Baidu AI Studio Develop platform. Through the autonomous control scheme design and the non-stop study on the deep learning algorithm, an intelligent vehicle model based on PaddlePaddle deep learning is here. The vehicle has the function of automatic driving on the simulated track. In addition, it can distinguish several traffic signs and make feedbacks accordingly.