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Title: An efficient and scalable simulation model for autonomous vehicles with economical hardware
Authors: Sajjad, Muhammad
Irfan, Muhammad
Muhammad, Khan
Ser, Javier Del
Sánchez Medina, Javier Jesús 
Andreev, Sergey
Ding, Weiping
Lee, Jong Weon
UNESCO Clasification: 332703 Sistemas de transito urbano
120326 Simulación
Keywords: Autonomous driving
Raspberry Pi
Scalar-visual sensor
Intelligent transportation systems
Issue Date: 2021
Journal: IEEE Transactions on Intelligent Transportation Systems 
Abstract: 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 proposed 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 realtime response on a resource-constrained device without significant overheads, thus making it a suitable candidate for autonomous driving in current intelligent transportation systems.
ISSN: 1524-9050
DOI: 10.1109/TITS.2020.2980855
Source: IEEE Transactions on Intelligent Transportation Systems [ISSN 1524-9050], v. 22 (3), p. 1718-1732, (Marzo 2021)
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