As automated vehicle technology advances, measures for their safe containment become increasingly important. To this end, geofencing is a prominent alternative as a fundamental technique for triggering specific actions when vehicles enter or leave a predefined operational area. Today’s geofencing methods usually fall short in safety-critical use cases, failing to contain vehicles, or triggering needless intervening actions. This work presents the novel model predictive geofence, which predicts future transgressions based on vehicle dynamics-informed real-time decisions. We studied its performance compared to representative approaches, both physically at the AstaZero Proving Ground in Sweden and through numerical calculations. Our geofence utilised the operational area more effectively than current approaches. Furthermore, the model predictive geofence successfully contained the vehicle to the operational area in all experiments, preventing exit with a low amount of false stops. The model predictive geofence presents an applicable approach for quick decision-making regarding the containment of vehicles in operational areas.