TrACT：一种面向长尾轨迹预测的训练动态感知对比学习框架
TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
AI总结：
针对长尾场景下轨迹预测性能下降问题，提出训练动态感知原型对比学习框架TrACT，利用输出误差聚类并构建原型进行重训练，在精度和场景合规性上达到最先进水平。
摘要：
作为一项安全关键任务，自动驾驶需要对道路使用者的未来轨迹进行准确预测，以便在具有挑战性的条件下实现安全运动规划。然而，许多近期的深度学习方法在挑战性场景中性能下降，主要原因是这些场景在训练数据中出现频率较低。为了解决这一长尾问题，现有方法在训练过程中将挑战性场景在特征空间中拉近，以触发它们之间的信息共享，从而实现更鲁棒的学习。然而，这些方法主要依赖运动模式来刻画场景，忽略了更具信息量的上下文信息，例如交互和场景布局。我们认为，利用这些信息不仅能提高预测精度，还能提升生成轨迹的场景合规性。本文提出将更丰富的训练动态信息引入原型对比学习框架。具体而言，我们提出一个两阶段过程。首先，使用基线编码器-解码器框架生成丰富的上下文特征。利用训练动态信息，根据模型输出误差将这些特征划分为多个簇，并在每个簇内计算一个原型。其次，在对比学习框架中使用这些原型重新训练模型。我们使用两个大规模自然驾驶数据集对所提方法进行了实证评估，结果表明，我们的方法通过提高长尾样本上的精度和场景合规性，实现了最先进的性能。此外，我们对部分簇进行了实验，以突出我们的方法在减少训练偏差方面的额外优势。
英文摘要：
As a safety critical task, autonomous driving requires accurate predictions of road users' future trajectories for safe motion planning, particularly under challenging conditions. Yet, many recent deep learning methods suffer from a degraded performance on the challenging scenarios, mainly because these scenarios appear less frequently in the training data. To address such a long-tail issue, existing methods force challenging scenarios closer together in the feature space during training to trigger information sharing among them for more robust learning. These methods, however, primarily rely on the motion patterns to characterize scenarios, omitting more informative contextual information, such as interactions and scene layout. We argue that exploiting such information not only improves prediction accuracy but also scene compliance of the generated trajectories. In this paper, we propose to incorporate richer training dynamics information into a prototypical contrastive learning framework. More specifically, we propose a two-stage process. First, we generate rich contextual features using a baseline encoder-decoder framework. These features are split into clusters based on the model's output errors, using the training dynamics information, and a prototype is computed within each cluster. Second, we retrain the model using the prototypes in a contrastive learning framework. We conduct empirical evaluations of our approach using two large-scale naturalistic datasets and show that our method achieves state-of-the-art performance by improving accuracy and scene compliance on the long-tail samples. Furthermore, we perform experiments on a subset of the clusters to highlight the additional benefit of our approach in reducing training bias.
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。
