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Deep vision for navigation of autonomous motorcycle in urban and semi-urban environments

Mohammadkhani, M. A ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/ICSPIS48872.2019.9066130
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. Deep neural networks are currently the best solution for road and traffic scene interpretation for autonomous and self-driving vehicles. Compared to the autonomous cars, motorcycles have significant flexibility and advantages in crowded traffic situations and especially in non-urban and off-road areas. Many off-road tracks especially for agriculture and environment management tasks are only traversable with motorcycles. In this paper, a deep neural network is used for design and implementation of the vision system for navigation of an autonomous motorcycle. The proposed framework is evaluated using real world scenarios captured by a real motorcycle in various complex situations. The experimental results show that the proposed framework is capable of highly accurate interpretation of various environments for autonomous navigation of a motorcycle. © 2019 IEEE
  6. Keywords:
  7. Agricultural robots ; Intelligent systems ; Motorcycles ; Navigation ; Off road vehicles ; Road vehicles ; Roads and streets ; Signal processing ; Autonomous navigation ; Design and implementations ; Environment management ; Highly accurate ; Real-world scenario ; Traffic situations ; Urban environments ; Vision systems ; Deep neural networks
  8. Source: 5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019, 18 December 2019 through 19 December 2019 ; 2019 ; 9781728153506 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/9066130