TEACar: An Open-Source Autonomous Driving Platform
Provides an accessible, modular testbed for researchers and educators to validate vision-based perception and learning-based control in autonomous driving.
TEACar is a modular, open-source 1/14- to 1/16-scale autonomous driving platform with a four-layer deck structure, ROS 2-based software, and hardware abstraction. Evaluated with three CNN-based steering controllers, it demonstrated scalable, cost-effective performance for ITS research.
Intelligent Transportation Systems (ITS) increasingly rely on vision-based perception and learning-based control, necessitating experimental platforms that support realistic hardware-in-the-loop validation. Small-scale platforms for autonomous racing offer a practical path to hardware validation, but often suffer from limited modularity, high integration complexity, or restricted extensibility. This paper presents TEACAR, a 1/14- to 1/16-scale autonomous driving platform designed with modular mechanical architecture, hardware abstraction, and ROS 2-based software. The system adopts a four-layer deck structure that physically decouples sensing, computation, actuation, and power subsystems, improving structural rigidity while simplifying reconfiguration. We constructed and comprehensively evaluated the prototype of TEACAR. Its mechanical stability, structural characteristics, and software performance were quantified based on three CNN-based steering controllers. Inference latency, power consumption, and system operating time were measured to evaluate computational capability and robustness. Our experiments demonstrated that TEACAR offers a scalable, modular, and cost-effective testbed for ITS research, education, and development. Our project repository is available on GitHub.