融合知识先验的车辆轨迹预测深度学习算法
收稿日期: 2024-01-12
修回日期: 2024-05-08
网络出版日期: 2024-06-04
基金资助
中国科学院战略性先导科技专项(XDA27000000)
Knowledge-infused deep learning algorithm for vehicle trajectory prediction
Received date: 2024-01-12
Revised date: 2024-05-08
Online published: 2024-06-04
车辆轨迹预测算法在自动驾驶领域扮演着关键角色。当前基于深度学习的算法虽然能够显著提升车辆轨迹预测的准确性，但缺乏算法决策过程的可解释性。为此，本文将知识先验融入深度学习算法中，提出一个基于注意力网络的轨迹预测算法KIT。与传统的依赖增加约束来融合知识的方法不同，该算法通过设计特定的模型结构来融合社会力模型的先验知识，模仿驾驶者在复杂交通中的决策过程，从而增加预测过程的可解释性。KIT使用注意力机制模拟驾驶者观察周边环境，并通过多层感知机网络预估驾驶者基于自身动机、周边车辆和环境条件的加速度变化。在Argoverse数据集上的测试显示，KIT在增加可解释性的同时，其预测效果也超过了其他先进的深度学习轨迹预测算法。
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 . DOI: 10.7523/j.ucas.2024.045
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.
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