  We present Contingency Model Predictive Control (CMPC), a motion planning and
control framework that optimizes performance objectives while simultaneously
maintaining a contingency plan -- an alternate trajectory that avoids a
potential hazard. By preserving the existence of a feasible avoidance
trajectory, CMPC anticipates emergency and keeps the controlled system in a
safe state that is selectively robust to the identified hazard. We accomplish
this by adding an additional prediction horizon in parallel to the typical
Model Predictive Control (MPC) horizon. This extra horizon is constrained to
guarantee safety from the contingent threat and is coupled to the nominal
horizon at its first command. Thus, the two horizons negotiate to compute
commands that are both optimized for performance and robust to the contingent
event. This article presents a linear formulation for CMPC, illustrates its key
features on a toy problem, and then demonstrates its efficacy experimentally on
a full-size automated road vehicle that encounters a realistic pop-out
obstacle. Contingency MPC approaches potential emergencies with safe,
intuitive, and interpretable behavior that balances conservatism with incentive
for high performance operation.
