基于信息分形的行人轨迹预测方法

行人轨迹预测应用十分广泛，比如自动驾驶、机器人导航等。在轨迹预测中，一些不确定信息给轨迹预测任务带来了挑战，比如判别器中对轨迹信息判别的不确定，复杂的交互信息。在不确定信息处理科学领域，信息分形能有效处理不确定信息的不确定性和复杂性。受此启发，为了充分处理判别器中轨迹信息判别的不确定性，提升预测精度，该文提出了基于信息分形的轨迹预测方法。首先，场景信息和历史轨迹信息被特征提取模块提取。然后，通过注意力模块获取到场景-行人之间的交互信息与行人-行人之间的交互信息。最后基于生成对抗网络和信息分形生成合理的轨迹。在两个公共数据集ETH/UCY上实验表明，该方法能有效处理轨迹信息的不确定性，提高轨迹预测的精度。比如突然转弯、从后方超越前人、避让等行为的轨迹都能有效预测。在平均位移误差(ADE)和终点位移误差(FDE)上相比基准模型误差平均降低了11.11%和23.48%。

Pedestrian Trajectory Prediction Method Based on Information Fractals

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.