A Comparative Study of Scaled Autonomous Vehicle Platforms for Research and Education Academic Article in Scopus uri icon

abstract

  • This paper provides a comparative analysis of three scaled autonomous vehicle platforms-Quanser QCar, MIT RACECAR (and its derivatives), and SunFounder PiCar-widely adopted in research, educational projects, and competition environments. We examine their hardware configurations, computational capabilities, and sensor arrays, as well as document a range of recent academic applications that utilize each platform's strengths. Our findings show that the QCar, featuring a high-performance NVIDIA Jetson module and comprehensive sensor package, excels in tasks requiring advanced processing, complex control, or multi-robot experimentation. The MIT RACECAR, based on a Traxxas chassis, offers an open-source environment and robust design suitable for real-time AI and agile driving competitions, while its derivatives extend customization for alternative sensor setups. Meanwhile, the SunFounder PiCar remains highly accessible in both cost and configurability, making it a popular choice for introductory-level courses and smaller-scale prototype development. By highlighting the distinct features, trade-offs, and use cases of these platforms, we aim to guide researchers, educators, and industry practitioners toward selecting the most suitable scaled vehicle for their autonomous systems projects. © 2025 IEEE.

publication date

  • January 1, 2025