CIDTL: A Cross-In Domain Transfer Learning Model for Vehicular Trajectory Prediction Under Few-Shot Conditions of Aggressive Driving Behavior

Data-driven methods have demonstrated high trajectory prediction accuracy with advancements in trajectory monitoring technology and deep learning algorithms. Human driving behavior shows significant heterogeneity, and aggressive driving behavior poses a significant threat to traffic safety. However, the aggressive trajectory data are usually scarce and few-shot, which makes it difficult to model. A cross-in domain transfer learning model (CIDTL) tailored for vehicular trajectory prediction under few-shot conditions of aggressive driving behavior is proposed to tackle the above challenges. CIDTL aligns cross-domain features from different road intersections and in-domain features of three types of driving behaviors, which can improve the prediction performance on few-shot samples in the target domain by leveraging sufficient data from source domains. Specifically, we develop a simple yet efficient method for driving behavior clustering, dividing trajectory data into three distinct styles. Then, CIDTL is proposed to align cross-domain features and in-domain features simultaneously. To evaluate the effectiveness of our proposed model, we conduct numerical experiments on the inD dataset. The results show that accurate predictions can be achieved even with limited aggressive driving behavior data, which indicates that CIDTL can successfully reduce the distribution difference of trajectory data between ordinary driving behavior and aggressive driving behavior, as well as the difference between source domains and target domain, by learning common driving knowledge and domain-invariant knowledge. The proposed model is robust and has good generalization, which expands the application scope of traditional trajectory prediction models in reality.