  This paper presents a novel data-driven approach to vehicle motion planning
and control in off-road driving scenarios. For autonomous off-road driving,
environmental conditions impact terrain traversability as a function of
weather, surface composition, and slope. Geographical information system (GIS)
and National Centers for Environmental Information datasets are processed to
provide this information for interactive planning and control system elements.
A top-level global route planner (GRP) defines optimal waypoints using dynamic
programming (DP). A local path planner (LPP) computes a desired trajectory
between waypoints such that infeasible control states and collisions with
obstacles are avoided. The LPP also updates the GRP with real-time sensing and
control data. A low-level feedback controller applies feedback linearization to
asymptotically track the specified LPP trajectory. Autonomous driving
simulation results are presented for traversal of terrains in Oregon and
Indiana case studies.
