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1 August 2023 An adaptive inherent bias correction strategy embedded with spatio-temporal attention network for pedestrian trajectory prediction
Jingmin Xi, Jie Chen, Zhixiang Huang, Yingjian Deng, Bin Li, Haitao Wang
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Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541H (2023) https://doi.org/10.1117/12.2684205
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China
Abstract
Pedestrian trajectory prediction is a key prerequisite for path planning and decision-making in autonomous driving. Most of the works based on graph neural network only deal with the dimension of spatio-temporal fusion, ignoring the trajectory features of individual pedestrians and the interaction features between pedestrians. In addition, the uncertainty of pedestrian movement and the potential inherent bias caused by the dynamic changes of surrounding environment and other factors are also rarely considered. We propose an bias correction strategy incorporating spatiotemporal attention networks to mitigate the negative impact of inherent bias on predictions in real environments. First, we extract temporal dependencies and pedestrian interactions in temporal and spatial dimensions, respectively, and construct a bias tensor to model the inherent bias; further, the bias tensor information is decoded, and then the deviation calculation is performed with the decoded original trajectory information, and then the correction of the trajectory prediction is realized. Experimental results on ETH and UCY datasets demonstrate the superiority of our strategy.
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Jingmin Xi, Jie Chen, Zhixiang Huang, Yingjian Deng, Bin Li, and Haitao Wang "An adaptive inherent bias correction strategy embedded with spatio-temporal attention network for pedestrian trajectory prediction", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541H (1 August 2023); https://doi.org/10.1117/12.2684205
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KEYWORDS
Bias correction
Deep learning
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