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 teams with diverse skills to retrofit the vehicles and test them in dedicated testing facilities. Testing the limits of safety and performance on such vehicles is costly and hazardous. It is also outside the reach of most academic departments and research groups. On the other hand, scaled-down 1/10th-1/16th scale vehicle platforms are more affordable but have limited similitude in dynamics, control, 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 development and deployment of algorithms 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 in autonomous systems research. 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：当学术研究者开发和验证自动驾驶算法时，高性能能力与车辆平台成本及复杂性之间的平衡始终是一大挑战。当前多数自动驾驶车辆（AV）研究局限于使用昂贵的商用车辆进行实验，这类车辆需要具备多元专业背景的大型团队进行改装，并在专用测试设施中开展验证工作。在这类车辆上测试安全与性能极限不仅成本高昂且存在危险性，更超出了大多数学术院系和研究团队的承受范围。另一方面，1/10至1/16比例缩小的车辆平台虽更具经济性，但在动力学、控制特性和驾驶性能方面存在有限相似性。为解决这一难题，本文提出采用开源机电设计并搭载全功能自主驾驶软件的三分之一比例自主电动卡丁车平台。该平台的多模态驾驶系统支持人工驾驶、自动驾驶和远程遥控三种模式，配备灵活感知套件可供感知、定位、规划与控制算法的开发部署。此项研究作为全尺寸车辆与缩小比例车型之间的桥梁，可加速自主系统研究中经济高效的算法进展。实验结果表明，AV4EV平台具备卓越性能与易用性，适用于新型自动驾驶算法开发。相关资源已全部公开于AV4EV.org，旨在促进自动驾驶与电动汽车领域的协同创新。