TEACAR是可模块化重构的1/14-1/16尺度自动驾驶平台，支持真实硬件验证。
TEACar: An Open-Source Autonomous Driving Platform
- 四层结构物理分离感知、计算、执行与供电系统，提升刚性并简化改装。
- 实测显示推理延迟低、功耗小、续航长，满足复杂控制需求。
- 适合智能交通研究、教学及开发，开源且成本可控。
智能交通系统日益依赖基于视觉的感知与学习驱动的控制，亟需支持真实硬件在环验证的实验平台。小型自动驾驶竞速平台为硬件验证提供了可行路径，但普遍存在模块化不足、集成复杂或扩展性差的问题。本文提出TEACAR，一个1/14至1/16比例的自主驾驶平台，采用模块化机械架构、硬件抽象和基于ROS 2的软件系统。系统采用四层甲板结构，物理上解耦感知、计算、执行与电源子系统，提升结构刚性的同时简化重构。我们构建并全面评估了TEACAR原型机，基于三种基于CNN的转向控制器量化了其机械稳定性、结构特性与软件性能。通过测量推理延迟、功耗与系统运行时间，评估了计算能力与鲁棒性。实验表明，TEACAR提供了一个可扩展、模块化且低成本的智能交通系统研究、教育与开发测试平台。项目仓库已开源在GitHub。
原文摘要 · Abstract (English)
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.
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