融合知识先验的车辆轨迹预测深度学习算法OA

In the field of autonomous driving,accurate vehicle trajectory prediction plays a crucial role.While current deep learning-based algorithms have significantly improved the accuracy of vehicle trajectory prediction,they lack interpretability regarding the decision-making process of the algorithm.To address this issue,we incorporate prior knowledge into the deep learning-based algorithm and propose a trajectory prediction algorithm based on attention mechanisms.Diverging from traditional methods that add constraints for knowledge integration,we employ a tailored model architecture that embeds insights from the social force model to replicate the decision-making processes of drivers in complex traffic scenarios,thereby enhancing the interpretability of the predictions.Knowledge-infused trajectory prediction algorithm(KIT)leverages an attention mechanism to imitate drivers' perception of their environment and uses a multilayer perceptron network for predicting accelerations influenced by the driver's intentions,nearby traffic,and surrounding roads.The proposed method is validated on the Argoverse dataset,and the results indicate that KIT demonstrates superior predictive performance compared to current advanced trajectory prediction methods.