TEACar: An Open-Source Autonomous Driving Platform

TL;DR

TEACAR is a scalable, modular, and cost-effective open-source small-scale autonomous driving platform designed for ITS research, featuring a four-layer structure and ROS 2 software for hardware-in-the-loop validation.

Contribution

The paper introduces TEACAR, a novel modular small-scale autonomous driving platform with a four-layer architecture and ROS 2 software, enabling realistic hardware validation.

Findings

TEACAR demonstrates high mechanical stability and modularity.

System performance evaluated with CNN-based steering controllers.

Quantified inference latency, power consumption, and robustness.

Abstract

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.