Learning Scheduling Method for Mixed Traffic at Autonomous Intersections Without Reliable Explicit Turn Information of Human-Driven Vehicles

At autonomous intersections in mixed traffic, where Connected and Autonomous Vehicles (CAVs) coexist with Human-Driven Vehicles (HDVs), scheduling must remain effective even when a reliable explicit HDV turning intention is not available sufficiently early for the scheduling decision. This paper proposes a learning-based platoon scheduling method for this information-limited setting. CAVs and HDVs are organized into mixed platoons or an HDV group, and a state-augmentation function is designed to encode their temporal relationship while preserving the priority of uncontrollable HDVs. An enumeration (EN)-based expert demonstration mechanism is further integrated into the training process to provide high-quality experience. In the representative training run reported in the manuscript, the expert-assisted model achieved a peak average reward approximately 14% higher than the model trained without demonstrations. Across the tested demand cases, the learned scheduler also obtained travel-cost performance close to the EN benchmark. The scope of the method is explicitly limited to the modeled lane-keeping and sensing assumptions; robustness to random seeds, unexpected HDV maneuvers, communication delays, and additional safety metrics requires dedicated validation.