CCF：用于行人轨迹预测的交叉校正框架
CCF: Cross Correcting Framework for Pedestrian Trajectory Prediction
AI总结：
提出交叉校正框架（CCF），通过双子网相互校正机制学习时空表示，结合轨迹预测与分类任务，在ETH-UCY和SDD数据集上精确预测行人轨迹。
摘要：
准确预测未来行人轨迹在各个领域至关重要。由于未来行人轨迹的不确定性，在多智能体场景中学习复杂的时空表示非常重要。为了解决这个问题，我们提出了一种新颖的交叉校正框架（Cross-Correction Framework, CCF），以更好地学习行人轨迹的时空表示。我们的框架由两个轨迹预测模型（称为子网）组成，它们共享相同的架构，并使用交叉校正损失和轨迹预测损失进行训练。交叉校正利用两个子网的学习，使它们能够通过相互校正机制细化其底层的轨迹表示。具体来说，我们使用交叉校正损失来学习如何通过子网间的交互来相互校正。为了在子网之间引入多样化学习，我们使用神经网络生成的变换后的观测轨迹作为一个子网的输入，并将原始观测轨迹作为另一个子网的输入。我们为每个子网利用基于Transformer的编码器-解码器架构来捕捉行人的运动和社交互动。Transformer的编码器捕捉轨迹中的运动模式，而解码器则关注行人与邻居的互动。每个子网执行预测未来轨迹的主任务（回归任务）以及对预测轨迹进行分类的次任务（分类任务）。在ETH-UCY和SDD等真实世界基准数据集上的大量实验证明了我们提出的框架CCF在精确预测行人未来轨迹方面的有效性。我们还进行了几项消融实验，以证明我们方法中使用的各种模块和损失函数的有效性。
英文摘要：
Accurately predicting future pedestrian trajectories is crucial across various domains. Due to the uncertainty in future pedestrian trajectories, it is important to learn complex spatio-temporal representations in multi-agent scenarios. To address this, we propose a novel Cross-Correction Framework (CCF) to learn spatio-temporal representations of pedestrian trajectories better. Our framework consists of two trajectory prediction models, known as subnets, which share the same architecture and are trained with both cross-correction loss and trajectory prediction loss. Cross-correction leverages the learning from both subnets and enables them to refine their underlying representations of trajectories through a mutual correction mechanism. Specifically, we use the cross-correction loss to learn how to correct each other through an inter-subnet interaction. To induce diverse learning among the subnets, we use the transformed observed trajectories produced by a neural network as input to one subnet and the original observed trajectories as input to the other subnet. We utilize transformer-based encoder-decoder architecture for each subnet to capture motion and social interaction among pedestrians. The encoder of the transformer captures motion patterns in trajectories, while the decoder focuses on pedestrian interactions with neighbors. Each subnet performs the primary task of predicting future trajectories (a regression task) along with the secondary task of classifying the predicted trajectories (a classification task). Extensive experiments on real-world benchmark datasets such as ETH-UCY and SDD demonstrate the efficacy of our proposed framework, CCF, in precisely predicting pedestrian future trajectories. We also conducted several ablation experiments to demonstrate the effectiveness of various modules and loss functions used in our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
