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Off-road vehicles are susceptible to rollovers in terrains with largeelevation features, such as steep hills, ditches, and berms. One way to protectthem against rollovers is ruggedization through the use of industrial-gradeparts and physical modifications. However, this solution can be prohibitivelyexpensive for academic research labs. Our key insight is that a software-basedrollover-prevention system (RPS) enables the use of commercial-off-the-shelfhardware parts that are cheaper than their industrial counterparts, thusreducing overall cost. In this paper, we present HOUND, a small-scale,inexpensive, off-road autonomy platform that can handle challenging outdoorterrains at high speeds through the integration of an RPS. HOUND is integratedwith a complete stack for perception and control, geared towards aggressiveoffroad driving. We deploy HOUND in the real world, at high speeds, on fourdifferent terrains covering 50 km of driving and highlight its utility inpreventing rollovers and traversing difficult terrain. Additionally, throughintegration with BeamNG, a state-of-the-art driving simulator, we demonstrate asignificant reduction in rollovers without compromising turning ability acrossa series of simulated experiments. Supplementary material can be found on ourwebsite, where we will also release all design documents for the platform:https://sites.google.com/view/prl-hound .

HOUND: An Open-Source, Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving