中国科学院大学学报2026，Vol.43Issue(2)：209-217,9.DOI:10.7523/j.ucas.2024.045
融合知识先验的车辆轨迹预测深度学习算法
Knowledge-infused deep learning algorithm for vehicle trajectory prediction
摘要
Abstract
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.关键词
自动驾驶/车辆轨迹预测/先验知识/深度学习Key words
autonomous driving/vehicle trajectory prediction/prior knowledge/deep learning分类
交通工程引用本文复制引用
姜翠,焦建彬..融合知识先验的车辆轨迹预测深度学习算法[J].中国科学院大学学报,2026,43(2):209-217,9.基金项目
中国科学院战略性先导科技专项(XDA27000000)资助 （XDA27000000）
