同济大学学报（自然科学版）2024，Vol.52Issue(7)：1099-1108,10.DOI:10.11908/j.issn.0253-374x.22349
领域知识与数据驱动的混合交通流车辆轨迹预测
Mixed Traffic Flow Trajectory Prediction Method Driven by Domain Knowledge and Data
摘要
Abstract
Autonomous vehicles need to have the ability to predict the trajectory of vehicles around them.There are many mixed traffic flow roads with weak rules and strong interactions in developing countries,and trajectory prediction of high-density traffic flows is an extremely challenging task.In order to predict the trajectory with accuracy and interpretability for mixed traffic flow,a domain knowledge-guided convolutional long short-term memory(DK-Conv-LSTM)to realize the long and short-term trajectory prediction was proposed.In the data-driven model,a convolutional layer(Conv)was used to extract crucial information from interactive vehicles,and the long short-term memory(LSTM)was utilized to predict trajectory after the concatenation of the history information of the vehicle.Knowledge expertise guided the training of deep learning models by being embedded in loss functions.Using the basic LSTM as the benchmark,the Conv-LSTM with only the convolutional structure added,reduces the final displacement error(FDE)by 30.46%and the average displacement error(ADE)by 34.78%.The DK-Conv-LSTM reduced the FDE by 46.81%and ADE by 49.08%.Moreover,it could recreate complex driving behavior trajectories such as following between two vehicles and overtaking.关键词
交通工程/轨迹预测/混合交通流/深度学习模型/知识Key words
traffic engineering/trajectory prediction/mixed traffic flow/deep learning model/domain-knowledge分类
交通工程引用本文复制引用
刘晗,孙剑..领域知识与数据驱动的混合交通流车辆轨迹预测[J].同济大学学报（自然科学版）,2024,52(7):1099-1108,10.基金项目
国家自然科学基金(52125208),国家重点研发计划(2019YFB1600200) （52125208）
