When academic researchers develop and validate autonomous driving algorithms, there is a challenge in balancing high-performance capabilities with the cost and complexity of the vehicle platform. Much of today's research on autonomous vehicles (AV) is limited to experimentation on expensive commercial vehicles that require large skilled teams to retrofit the vehicles and test them in dedicated facilities. On the other hand, 1/10th-1/16th scaled-down vehicle platforms are more affordable but have limited similitude in performance and drivability. To address this issue, we present the design of a one-third-scale autonomous electric go-kart platform with open-source mechatronics design along with fully functional autonomous driving software. The platform's multi-modal driving system is capable of manual, autonomous, and teleoperation driving modes. It also features a flexible sensing suite for the algorithm deployment across perception, localization, planning, and control. This development serves as a bridge between full-scale vehicles and reduced-scale cars while accelerating cost-effective algorithmic advancements. Our experimental results demonstrate the AV4EV platform's capabilities and ease of use for developing new AV algorithms. All materials are available at AV4EV.org to stimulate collaborative efforts within the AV and electric vehicle (EV) communities.

翻译：学术研究者开发与验证自动驾驶算法时，常面临高性能需求与车辆平台成本及复杂性之间的平衡挑战。当前大多数自动驾驶车辆（AV）研究局限于使用昂贵的商用车辆进行实验，这些车辆需要大型专业团队进行改装并在专用设施中测试。而1/10至1/16缩比车辆平台虽成本较低，但在性能和操控性方面存在显著的相似度不足。为解决这一问题，我们提出一种三分之一比例的自主电动卡丁车平台设计，包含开源机电一体化设计及功能完备的自主驾驶软件。该平台的多模式驾驶系统支持手动、自主及远程操控三种驾驶模式，并配备灵活的传感器套件，可部署感知、定位、规划与控制等算法。本开发在全尺寸车辆与缩比模型车之间架起桥梁，同时加速了低成本算法创新。实验结果表明，AV4EV平台在开发新型自动驾驶算法方面兼具卓越性能与易用性。所有资料已在AV4EV.org公开，旨在促进自动驾驶与电动汽车领域的协作研究。