A Comparative Study of Scaled Autonomous Vehicle Platforms for Research and Education

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.