SKGACN:Social Knowledge-guided Graph Attention Convolutional Network for Human Trajectory Prediction

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Lv, Kai, Yuan, Liang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Convolution; graph attention; Pedestrian trajectory prediction; Pedestrians; Prediction models; Predictive models; Social factors; Social interaction; social knowledge-guided; Spatiotemporal phenomena; State-of-the-art reviews; Symbols; temporal convolution; Trajectory; Visualization
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3283544

Pedestrian trajectory prediction is crucial in driverless applications. To accurately predict the high-quality trajectory of pedestrians, it is necessary to consider the reasonable social interaction and the spatiotemporal relationships between pedestrians. Previous methods couldn't accurately capture the social features of pedestrians in realistic congested situations and extract spatiotemporal interaction features with high computation. Therefore, in this paper, a novel prediction model is proposed, called the Social Knowledge-guided Graph Attention Convolutional Network (SKGACN), which aims to address the social interactions and the spatiotemporal relationships between pedestrians with low computational requirements. Specifically, the social knowledge-guided graph attention mechanism fully considers multiple information relative to pedestrians to capture their social interaction. For spatiotemporal interactions, an improved Temporal Convolution Network (TCN) model is use as it can parallelize the processing times to get as higher efficiency compared to traditional models. Compared to the state-of-the-art methods, we evaluate our proposed method after applying it on two public datasets (ETH and UCY). The experimental results show that our method performs better in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics.