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Architecture design and implementation of image based autonomous car: THUNDER-1

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Abstract

Autonomous driving with high velocity is a research hotspot which challenges the scientists and engineers all over the world. This paper proposes a scheme of indoor autonomous car based on ROS which combines the method of Deep Learning using Convolutional Neural Network (CNN) with statistical approach using liDAR images and achieves a robust obstacle avoidance rate in cruise mode. In addition, the design and implementation of autonomous car are also presented in detail which involves the design of Software Framework, Hector Simultaneously Localization and Mapping (Hector SLAM) by Teleoperation, Autonomous Exploration, Path Plan, Pose Estimation, Command Processing, and Data Recording (Co- collection). what’s more, the schemes of outdoor autonomous car, communication, and security are also discussed. Finally, all functional modules are integrated in nVidia Jetson TX1.

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Acknowledgements

The research of autonomous car are funded by Sci-Tech Support Plan of Sichuan Province, China [Grant Numbers: 2016GZ0343].

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Authors and Affiliations

  1. School of Cybersecurity, Chengdu University of Information Technology, Chengdu, China

    Chengmin Zhou & Fei Li

  2. School of Information Engineering, Southwest University of Science and Technology, Mianyang, China

    Wen Cao

Authors
  1. Chengmin Zhou
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  2. Fei Li
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  3. Wen Cao
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Correspondence to Chengmin Zhou.

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Zhou, C., Li, F. & Cao, W. Architecture design and implementation of image based autonomous car: THUNDER-1. Multimed Tools Appl 78, 28557–28573 (2019). https://doi.org/10.1007/s11042-018-5816-9

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  • DOI: https://doi.org/10.1007/s11042-018-5816-9

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