Abstract Semi-Autonomous Systems (SAS) encapsulate a stochastic decision process explicitly controlled by both an agent and a human, in order to leverage the distinct capabilities of each actor. Planning in SAS must address the challenge of transferring control quickly, safely, and smoothly back-and-forth between the agent and the human. We formally define SAS and the requirements to guarantee that the controlling entities are always able to act competently. We then consider applying the model to Semi-Autonomous VEhicles (SAVE), using a hierarchical approach in which micro-level transferof-control actions are governed by a high-fidelity POMDP model. Macro-level path planning in our hierarchical approach is performed by solving a Stochastic Shortest Path (SSP) problem. We analyze the integrated model and show that it provides the required guarantees. Finally, we test the SAVE model using real-world road data from Open Street Map (OSM) within 10 cities, showing the benefits of the collaboration between the agent and human.