• model predictive control
• occupancy grid
• autonomous driving

This thesis presents an integration of an Occupancy Grid with a Model Predic-
tive Control (MPC) framework for autonomous vehicles in highway scenarios. The
highway is represented as an evenly spaced field of cells. At first Stochastic MPC
(SMPC) planning considers the most likely maneuvers of other traffic participants
by assigning an occupancy probability to the cells that represent the highway. In
order the guarantee the safety and the property of recursive feasibility of the algo-
rithm, a second control is designed, called Failsafe MPC (FMPC). This one takes
into account the worst-case realization of other traffic participants. The combina-
tion of SMPC and FMPC provides an efficient, fast and feasible algorithm whose
results will be evaluated in a simulated environment.