Trajectory forecasting based on distilled transformer networks

In autonomous driving systems, trajectory prediction is crucial for enhancing roadway safety and diminishing the likelihood of accidents. However, over time, the evolution of the trajectory becomes more and more uncertain and unpredictable, increasing the complexity of the problem. To address these problems, this paper innovatively proposes a trajectory prediction framework named DistTF, which skillfully integrates Transformer model and knowledge distillation. DistTF uses Transformer model to characterize vehicle-to-vehicle interactions and mine deep timing patterns to train a high-performance teacher model. Subsequently, through knowledge distillation, the knowledge of the teacher model is effectively transferred to the smaller student model, so as to maintain considerable prediction accuracy under the premise of ensuring fast reasoning and low parameter quantity. Experimental results on the NGSIM dataset show that DistTF achieves the highest accuracy among the four algorithms participating in the comparison. Comprehensive comparative experiments verify the effectiveness of the proposed method.