An Efficient and Scalable Simulation Model for Autonomous Vehicles With Economical Hardware

Muhammad Sajjad, Muhammad Irfan, Khan Muhammad, Javier Del Ser, Javier Sanchez-Medina, Sergey Andreev, Weiping Ding, Jong Weon Lee

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Autonomous vehicles rely on sophisticated hardware and software technologies for acquiring holistic awareness of their immediate surroundings. Deep learning methods have effectively equipped modern self-driving cars with high levels of such awareness. However, their application requires high-end computational hardware, which makes utilization infeasible for the legacy vehicles that constitute most of today's automotive industry. Hence, it becomes inherently challenging to achieve high performance while at the same time maintaining adequate computational complexity. In this paper, a monocular vision and scalar sensor-based model car is designed and implemented to accomplish autonomous driving on a specified track by employing a lightweight deep learning model. It can identify various traffic signs based on a vision sensor as well as avoid obstacles by using an ultrasonic sensor. The developed car utilizes a single Raspberry Pi as its computational unit. In addition, our work investigates the behavior of economical hardware used to deploy deep learning models. In particular, we herein propose a novel, computationally efficient, and cost-effective approach. The designed system can serve as a platform to facilitate the development of economical technologies for autonomous vehicles that can be used as part of intelligent transportation or advanced driver assistance systems. The experimental results indicate that this model can achieve real-time response on a resource-constrained device without significant overheads, thus making it a suitable candidate for autonomous driving in current intelligent transportation systems.
Original languageEnglish
Pages (from-to)1718-1732
Number of pages15
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number3
Early online date2020
Publication statusPublished - Mar 2021
Publication typeA1 Journal article-refereed


  • Autonomous vehicles
  • Autonomous automobiles
  • Automobiles
  • Real-time systems
  • Machine learning
  • Companies
  • Hardware
  • Autonomous driving
  • Raspberry Pi
  • scalar-visual sensor
  • intelligent transportation systems.

Publication forum classification

  • Publication forum level 2


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