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智能体的轨迹预测是人工智能领域中的热点之一，特别是在自动驾驶领域，预测智能体下一时间点的位置是自动驾驶辅助系统的关键任务。在智能体轨迹预测技术的基础上，根据建模方法的不同进行分类介绍，分别为循环神经网络(Recurrent Neural Network, RNN)、卷积神经网络(Convolutional Neural Network, CNN)、生成对抗网络(Generative Adversarial Network, GAN)和混合网络，同时分析了常见的经典模型的优缺点，归纳了当前常用公开的轨迹预测数据集和评价指标，比较了经典模型的算法性能，对智能轨迹预测方向进行了展望和总结。
Abstract：The trajectory prediction of agents is one of the hot topics in the field of artificial intelligence, and predicting the agent's position at next time point is a crucial task of autonomous systems.Based on the agent trajectory prediction technology and according to the different modeling methods, Recursive Neural Network(RNN),Convolutional Neural Network(CNN),Generative Adversarial Network(GAN),and hybrid network are introduced.At the same time, the advantages and disadvantages of common classical models are analyzed.In addition, the current commonly-used public trajectory prediction datasets and evaluation metrics are summarized.Finally, the developing direction of intelligent trajectory prediction is prospected and summarized.
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基本信息:
DOI：
中图分类号:U463.6;TP18
引用信息:
[1]章璐璐,李思照.基于深度学习的智能体轨迹预测文献综述[J].无线电工程,2023,53(03):644-656.
基金信息:
黑龙江省自然科学基金(优青项目)(JJ2019YX0922); 基础科研项目(JCKY2020208B045)~~