PMI-Transformer: Parking Memory Interaction Transformer for Vehicle Intent Prediction via Cooperative Vehicle-Infrastructure Systems

Parking scenarios present huge challenges for the prediction of intentions and situational awareness of autonomous vehicles. Unlike urban roads, parking scenarios feature greater freedom of movement, denser interactions, and more diverse intents. To address this issue, we propose the Parking Memory Interaction Transformer (PMI-Transformer) for predicting vehicle intent and trajectory based on cooperative vehicle-infrastructure systems (CVIS). Our model utilizes long-term semantic observation data and short-term historical trajectories to predict a target vehicle’s future intentions and trajectories from roadside perception units in CVIS. Based on global spatiotemporal features, a memory encoding strategy extracts essential information from the vehicle’s long-term history. At the same time, a hypergraph-based multiscale interaction module captures dense, diverse interactions in multi-vehicle environments. We incorporate parking occupancy data and calculate affinities between the vehicle and nearby spots to better characterize vehicle intent within the parking context. Experimental results show that our model outperforms state-of-the-art methods in intent and trajectory prediction, with visualizations confirming that PMI-Transformer effectively captures key historical information and social interactions in parking scenarios.