弹道
计算机科学
人工智能
人工神经网络
构造(python库)
机器学习
模式识别(心理学)
天文
物理
程序设计语言
作者
Hong-Yu Hu,Qi Wang,Ming Cheng,Zhenhai Gao
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:24 (3): 2746-2759
标识
DOI:10.1109/tits.2022.3219874
摘要
High-precision vehicle trajectory prediction can enable autonomous vehicles to provide a safer and more comfortable trajectory planning and control. Unfortunately, current trajectory prediction methods have difficulty extracting hidden driving features across multiple time steps, which is important for long-term prediction. In order to solve this shortcoming, a temporal pattern attention-based trajectory prediction network, named TP2Net, was proposed, and vehicle of interest inception was established to construct an interaction model among vehicles. Experimental results show a 15% improvement in predictive performance over the previous best method under a 5-s prediction horizon. Moreover, in order to explain why temporal pattern attention was adopted and demonstrate its ability to extract hidden features that are intuitive to human beings, a layer interpretation module was included in TP2Net to quantify the mutual information contained between the input and the intermediate layer output tensor. The results of experiments using naturalistic trajectory datasets indicated that temporal pattern attention can extract three important stages in lane changing, showing that temporal pattern attention can effectively extract hidden features and improve prediction accuracy.
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