AV4EV: Open-Source Modular Autonomous Electric Vehicle Platform for Making Mobility Research Accessible

June 20, 2024

Zhijie Qiao, Mingyan Zhou, Zhijun Zhuang, Tejas Agarwal, Felix Jahncke, Po-Jen Wang, Jason Friedman, Hongyi Lai, Divyanshu Sahu, Tomáš Nagy, Martin Endler, Jason Schlessman, Rahul Mangharam

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 this http URL to stimulate collaborative efforts within the AV and electric vehicle (EV) communities.

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*This paper was nominated for Best Paper Finalist for the 2024 IEEE Intelligent Vehicles Symposium

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