  While the workspace of traditional ground vehicles is usually assumed to be
in a 2D plane, i.e., SE(2), such an assumption may not hold when they drive at
high speeds on unstructured off-road terrain: High-speed sharp turns on
high-friction surfaces may lead to vehicle rollover; Turning aggressively on
loose gravel or grass may violate the non-holonomic constraint and cause
significant lateral sliding; Driving quickly on rugged terrain will produce
extensive vibration along the vertical axis. Therefore, most offroad vehicles
are currently limited to drive only at low speeds to assure vehicle stability
and safety. In this work, we aim at empowering high-speed off-road vehicles
with competence awareness in SE(3) so that they can reason about the
consequences of taking aggressive maneuvers on different terrain with a 6-DoF
forward kinodynamic model. The model is learned from visual and inertial
Terrain Representation for Off-road Navigation (TRON) using multimodal,
self-supervised vehicle-terrain interactions. We demonstrate the efficacy of
our Competence-Aware High-Speed Off-Road (CAHSOR) navigation approach on a
physical ground robot in both an autonomous navigation and a human
shared-control setup and show that CAHSOR can efficiently reduce vehicle
instability by 62% while only compromising 8.6% average speed with the help of
TRON.
