Deep learning models play a crucial role in improving driver assistance systems and environmental perception. However, their tendency toward overconfident predictions poses risks to driver safety, particularly in adverse conditions. To address this, we propose AR-CP, an uncertainty-aware framework integrating conformal prediction and augmented reality (AR). AR-CP starts with a conformal prediction step, generating an uncertainty-aware prediction set. Then, AR simplifies and clarifies the visualization of the closest common parent class, reducing misinformation. We present a rigorous formulation and theoretical analysis, evaluating AR-CP on the ROAD dataset. Results demonstrate superior performance compared to existing methods, ensuring safer driving experiences with reduced mental load and heightened situation awareness, as validated by an immersive user study involving 15 participants.