弹道
计算机科学
行人
人工智能
机器学习
可视化
变压器
人类行为
工程类
物理
天文
运输工程
电气工程
电压
作者
Xian Zhong,Yan Xu,Zhengwei Yang,Wenxin Huang,Kui Jiang,Ryan Wen Liu,Zheng Wang
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-09-01
卷期号:24 (9): 9390-9400
被引量:1
标识
DOI:10.1109/tits.2023.3266762
摘要
Pedestrian trajectory prediction in multiple scenarios is of immense importance in autonomous driving and disentanglement of human behavior but is limited in catching human intention and initiative. Most previous works tend to predict the trajectory using only 2D coordinates, which generally cause two common problems: a) Overlooking the subjective initiative, including sudden swerve and erratic movement; b) A potential challenge called abnormal collision caused by unlabeled pedestrians on dataset is not being identified and resolved, which would ruin the model prediction. To break those limitations, we introduce visual localization and orientation as Visual Intention Knowledge to help the trajectory prediction, which is learned directly from visual scenarios. It benefits to comprehend human intention and formulates decision-making processes. Moreover, by learning from the visual information and decision-making policy, we construct the Visual Intention Knowledge associated spatio-temporal Transformer (VIKT) to predict human trajectory by combining the intention knowledge with the novel Transformer. Extensive experimental results demonstrate that our VIKT model could achieve competitive performance by the Visual Intention Knowledge through optimizing the model prediction compared with state-of-the-art methods in terms of prediction accuracy on ETH/UCY and SDD benchmarks.
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