重庆理工大学学报2024，Vol.38Issue(19)：21-27,7.DOI:10.3969/j.issn.1674-8425(z).2024.10.003
智能网联车辆MFP算法轨迹预测模型研究
Research on MFP algorithm-based trajectory prediction model for intelligent connected vehicles
摘要
Abstract
To address the low prediction accuracy in long-term trajectory prediction due to changing environments and interactive influences among vehicles,this paper proposes a multi-agent trajectory prediction model based on the Multiple Futures Predictor(MFP).First,the Symmetric Exponential Moving Average method is employed to remove outliers and smooth the trajectories.Then,the model utilizes Graph Convolutional Neural Network(GCN)for extracting interactive features between historical trajectories and future agents,encoding the interaction features.Finally,during the decoding process,the vehicle's own kinematic model is incorporated to generate dynamically feasible predicted trajectories.Experimental analysis is conducted on the publicly available NGSIM dataset.Our results demonstrate the model achieves trajectory prediction errors within 0.5 m.Compared to the results of other methods,the proposed model reduces ADE(Average Displacement Error)by 30.7％and FDE(Final Displacement Error)by 32.5％when predicting trajectories within 5 seconds,validating the effectiveness of the model and algorithm.关键词
自动驾驶/车辆轨迹预测/图神经网络/特征提取/MFP模型Key words
autonomous driving/vehicle trajectory prediction/graph neural networks/feature extraction/MFP model分类
交通工程引用本文复制引用
何博,黄妙华,刘若璎,邹天越,尹思源,胡永康..智能网联车辆MFP算法轨迹预测模型研究[J].重庆理工大学学报,2024,38(19):21-27,7.基金项目
国家重点研发计划(2018YFE0105500) （2018YFE0105500）
