A two-stage framework for parking search behavior prediction through adversarial inverse reinforcement learning and transformer.

Parking scenarios are spatially dense and have a lot of interactions, making predicting vehicles' search behavior crucial and challenging for autonomous driving. Existing data-driven prediction methods struggle to determine vehicles' intents and consider the surrounding environment accurately. This study proposes a novel two-stage framework for parking search behavior prediction, involving parking intent and vehicle trajectory predictions based on imitation learning and deep learning. First, we develop an adversarial inverse reinforcement learning model for parking search intent (PSI-AIRL) learning from measured trajectory data in an actual parking lot. Then, we design an integrated convolutional neural network (CNN) and transformer model to forecast vehicle trajectory using historical observations and the predicted parking search intents. This two-stage framework achieves parking search intent prediction by applying global information about the parking lot while improving vehicle trajectory prediction's accuracy and robustness. Finally, the experiments are conducted on the Dragon Lake Parking (DLP) dataset to compare our framework with state-of-the-art models. The results show that our model outperforms other baseline models in accuracy for parking intent and vehicle trajectory predictions. Moreover, our model shows exceptional accuracy and robustness in predicting across diverse parking scenarios. • A novel two-stage framework for parking search behavior prediction is proposed. • An AIRL model is developed for the first stage of parking intention learning. • Trajectory forecasting is then based on an integrated CNN and transformer model. • The experiments prove the superiority of our model via actual parking datasets. [ABSTRACT FROM AUTHOR]