Diffusion-SAFE: Shared Autonomy Framework with Diffusion for Safe Human-to-Robot Driving Handover

Safe handover in shared autonomy for vehicle control is well-established in modern vehicles. However, avoiding accidents often requires action several seconds in advance. This necessitates understanding human driver behavior and an expert control strategy for seamless intervention when a collision or unsafe state is predicted. We propose Diffusion-SAFE, a closed-loop shared autonomy framework leveraging diffusion models to: (1) predict human driving behavior for detection of potential risks, (2) generate safe expert trajectories, and (3) enable smooth handovers by blending human and expert policies over a short time horizon. Unlike prior works which use engineered score functions to rate driving performance, our approach enables both performance evaluation and optimal action sequence generation from demonstrations. By adjusting the forward and reverse processes of the diffusion-based copilot, our method ensures a gradual transition of control authority, by mimicking the drivers' behavior before intervention, which mitigates abrupt takeovers, leading to smooth transitions. We evaluated Diffusion-SAFE in both simulation (CarRacing-v0) and real-world (ROS-based race car), measuring human-driving similarity, safety, and computational efficiency. Results demonstrate a 98.5\% successful handover rate, highlighting the framework's effectiveness in progressively correcting human actions and continuously sampling optimal robot actions.