<p>Pedestrian detection is a fundamental component of autonomous mobility systems. However, among all pedestrians, those unaware of an approaching vehicle require particular attention. This paper proposes a diffusion-based pedestrian attention classification (DPAC) module that determines whether a pedestrian is aware of the ego vehicle in real-world driving scenarios. The proposed module takes bounding boxes from a detector as input and classifies the pedestrian’s attention state. Leveraging a diffusion model, the DPAC module maintains robust performance even under low-resolution inputs. It is lightweight in both computation time and memory usage, enabling real-time deployment directly following detection modules. The module is validated on the joint attention for autonomous driving (JAAD) dataset, which includes annotations for pedestrian attention. Experimental results demonstrate superior performance over existing methods. When attached to a detector, the complete system processes a 1920 × 1080 image—including detection and attention classification—within 33 ms. The results confirm both the effectiveness and scalability of the proposed DPAC module for integration into diverse perception pipelines.</p>