RT Journal Article
T1 An Efficient and Scalable Simulation Model for Autonomous Vehicles with Economical Hardware
A1 Sajjad, Muhammad
A1 Irfan, Muhammad
A1 Muhammad, Khan
A1 Ser, Javier Del
A1 Sanchez-Medina, Javier
A1 Andreev, Sergey
A1 Ding, Weiping
A1 Lee, Jong Weon
AB 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.
SN 1524-9050
YR 2021
FD 2021-03
LK https://hdl.handle.net/11556/5162
UL https://hdl.handle.net/11556/5162
LA eng
NO Sajjad , M , Irfan , M , Muhammad , K , Ser , J D , Sanchez-Medina , J , Andreev , S , Ding , W & Lee , J W 2021 , ' An Efficient and Scalable Simulation Model for Autonomous Vehicles with Economical Hardware ' , IEEE Transactions on Intelligent Transportation Systems , vol. 22 , no. 3 , 9094331 , pp. 1718-1732 . https://doi.org/10.1109/TITS.2020.2980855
NO Publisher Copyright: © 2000-2011 IEEE.
NO Manuscript received August 15, 2018; revised June 29, 2019, September 25, 2019, and December 16, 2019; accepted January 24, 2020. Date of publication May 15, 2020; date of current version March 1, 2021. This research was supported by the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19), the MSIT (Ministry of Science, ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00312) supervised by the IITP (Institute for Information & Communications Technology Promotion), the Natural Science Foundation of Jiangsu Province under Grant BK20191445, the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and in part by the RADIANT Project, Academy of Finland. The Associate Editor for this article was E. I. Vlahogianni. (Corresponding author: Khan Muhammad.) Muhammad Sajjad is with the Digital Image Processing Laboratory, Islamia College University, Peshawar 25000, Pakistan (e-mail: muhammad.sajjad@icp.edu.pk).
DS TECNALIA Publications
RD 28 sept 2024