Ridon Vehicle: Drive-by-Wire System for Scaled Vehicle Platform and Its Application on Behavior Cloning

Aws Khalil, Ahmed Abdelhamed, Girma Tewolde and Jaerock Kwon
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Aws Khalil: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Road, Dearborn, MI 48128-2406, USA
Ahmed Abdelhamed: Department of Electrical and Computer Engineering, Kettering University, 1700 University Avenue, Flint, MI 48504-6214, USA
Girma Tewolde: Department of Electrical and Computer Engineering, Kettering University, 1700 University Avenue, Flint, MI 48504-6214, USA
Jaerock Kwon: Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Road, Dearborn, MI 48128-2406, USA

Energies, 2021, vol. 14, issue 23, 1-25

Abstract: For autonomous driving research, using a scaled vehicle platform is a viable alternative compared to a full-scale vehicle. However, using embedded solutions such as small robotic platforms with differential driving or radio-controlled (RC) car-based platforms can be limiting on, for example, sensor package restrictions or computing challenges. Furthermore, for a given controller, specialized expertise and abilities are necessary. To address such problems, this paper proposes a feasible solution, the Ridon vehicle, which is a spacious ride-on automobile with high-driving electric power and a custom-designed drive-by-wire system powered by a full-scale machine-learning-ready computer. The major objective of this paper is to provide a thorough and appropriate method for constructing a cost-effective platform with a drive-by-wire system and sensor packages so that machine-learning-based algorithms can be tested and deployed on a scaled vehicle. The proposed platform employs a modular and hierarchical software architecture, with microcontroller programs handling the low-level motor controls and a graphics processing unit (GPU)-powered laptop computer processing the higher and more sophisticated algorithms. The Ridon vehicle platform is validated by employing it in a deep-learning-based behavioral cloning study. The suggested platform’s affordability and adaptability would benefit broader research and the education community.

Keywords: intelligent robots; mobile robots; robot design; robotics in intelligent vehicle and highway systems; mechatronic systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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