A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems

Zishuo Zhou, Zahid Akhtar, Ka Lok Man, Kamran Siddique*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. The proposed Internet of Things framework incorporates concepts and mechanisms from several domains of computer science, such as computer vision, artificial intelligence, sensor technology, and communication technology. The information captured by camera, such as road edges, traffic lights, and zebra lines, is highlighted using computer vision. The semantics of highlighted information is recognized by artificial intelligence. Sensors provide information on the direction and distance of obstacles, as well as their speed and moving direction. The communication technology is applied to share the information among the vehicles. Since vehicles have high probability to encounter accidents in congested locations, the proposed system enables vehicles to perform self-positioning with other vehicles in a certain range to reinforce their safety and stability. The empirical evaluation shows the viability and efficacy of the proposed system in such situations. Moreover, the collision time is decreased considerably compared with that when using traditional systems.

Original languageEnglish
JournalInternational Journal of Distributed Sensor Networks
Volume15
Issue number11
DOIs
Publication statusPublished - Nov 2019

Keywords

  • Autonomous vehicle
  • IoT
  • autonomous driving
  • convolutional neural networks
  • information sharing
  • platooning-based

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