Pedestrian trajectory prediction has been widely used in several fields, such as autonomous driving and robot navigation. In trajectory prediction, some uncertain information, such as the uncertainty of trajectory information discrimination in the discriminator and complex interactive information, bring challenges to the trajectory prediction task. In the field of uncertain information processing, information fractals can effectively deal with the uncertainty and complexity of uncertain information. Inspired by this, a trajectory prediction method based on the information fractal is proposed to fully deal with the uncertainty of trajectory information discrimination in the discriminator and improve the prediction accuracy. First, the scene and historical trajectory information are extracted by the feature extraction module. Subsequently, the scene-pedestrian interaction and pedestrian-pedestrian interaction information are obtained through the attention module.Finally, reasonable trajectories are generated using generative adversarial networks and information fractals.Experiments on the two public datasets ETH and UCY reveal that the proposed method can effectively deal with the uncertainty of trajectory information and improve the accuracy of trajectory prediction. For example,the trajectories of sudden turns, overtaking, avoidance, and other behaviors can be effectively predicted.Moreover, the Average Displacement Error (ADE) and Final Displacement Error (FDE) are reduced by an average of 11.11％ and 23.48％, respectively compared with the benchmark model error.
关键词
行人轨迹预测/不确定信息处理/信息分形/生成对抗网络
Key words
Pedestrian trajectory prediction/Uncertain information processing/Information fractal/Generative adversarial networks
维普
万方数据