期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 72, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2022.3229733
关键词
Trajectory; Predictive models; Convolution; Symmetric matrices; Decoding; Uncertainty; Matrix decomposition; Graph convolution network; pedestrian trajectory prediction; self-attention mechanism; spatiotemporal interactions; trajectory multimodality
Pedestrian trajectory prediction is an important research area that plays a crucial role in blind navigation, autonomous driving systems, and service robots. This field faces two challenges: modeling spatio-temporal interactions among pedestrians and dealing with the uncertainty of pedestrian trajectories. To address these challenges, we propose a graph convolution network that is aware of spatio-temporal interactions and trajectory distribution. Our model integrates a graph convolutional network and self-attention mechanism to model spatio-temporal interactions, and learns latent trajectory distribution information from measured trajectories at observed and future times. Experimental results demonstrate the effectiveness of our approach in predicting socially acceptable future trajectories.
Pedestrian trajectory prediction is a critical research area with numerous domains, e.g., blind navigation, autonomous driving systems, and service robots. There exist two challenges in this research field: spatio-temporal interaction modeling among pedestrians and the uncertainty of pedestrian trajectories. To tackle these challenges, we propose a spatio-temporal interaction aware and trajectory distribution aware graph convolution network. First, we propose a spatio-temporal interaction aware module that integrates a graph convolutional network and self-attention mechanism to model spatio-temporal interactions among pedestrians. Second, we design a trajectory distribution aware module to learn latent trajectory distribution information from the measured trajectories at observed and future times. This can provide knowledge-rich trajectory distribution information for the multimodality of the predicted trajectories. Finally, to address the problem of the propagation and accumulation of prediction errors, we design a trajectory decoder to generate the multimodal future trajectories. The proposed model is evaluated utilizing videos recorded by a camera sensor in crowded areas and can be applied to predict multiple pedestrians' future trajectories from in-vehicle cameras. Experimental results demonstrate that the proposed approach can achieve superior results on the average displacement error (ADE) and final displacement error (FDE) metrics to state-of-the-art approaches and can predict socially acceptable future trajectories.
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