  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.
