How to accurately predict pedestrian trajectories in multiple real-world scenarios is crucial for autonomous driving. In this paper, we propose a physical constraint module based on pedestrian velocity thresholds to improve the unreasonable trajectories predicted by the baseline model, while we introduce a new velocity discrepancy loss function to more efficiently inversely update the network weights while establishing the correlation between the pedestrian motion speeds in the historical time and the future time. Our proposed method is a plug-and-play module, and we evaluate the effectiveness of our method on the ETH and UCY datasets using two different baseline models.
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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