Predicting the states of dynamic traffic actors into the future is important for autonomous systems to operate safelyand efficiently. Remarkably, the most critical scenarios aremuch less frequent and more complex than the uncriticalones. Therefore, uncritical cases dominate the prediction. In this paper, we address specifically the challenging scenarios at the long tail of the dataset distribution. Our analysis shows that the common losses tend to place challenging cases suboptimally in the embedding space. As a consequence, we propose to supplement the usual loss with aloss that places challenging cases closer to each other. This triggers sharing information among challenging cases andlearning specific predictive features. We show on four public datasets that this leads to improved performance on the challenging scenarios while the overall performance stays stable. The approach is agnostic w.r.t. the used network architecture, input modality or viewpoint, and can be integrated into existing solutions easily. Code is available at https://github.com/lmb-freiburg/Contrastive-Future-Trajectory-Prediction
翻译:预测充满活力的交通行为者的未来状态对于自主系统安全高效运作非常重要。 值得注意的是,最关键的情况比非临界情况要少得多,更复杂。 因此, 预测中主要是一些非临界情况。 在本文中, 我们具体谈到数据集分布长尾的富有挑战性的假设情况。 我们的分析表明, 常见的损失往往在嵌入空间中将具有挑战性的案件置于次要地位。 因此, 我们提议用通常的损失来补充通常的损失, 使具有挑战性的案件彼此更加接近。 这触发了具有挑战性的案件之间的信息共享,并学习了具体的预测特征。 我们在四个公共数据集中显示,这导致在总体性能保持稳定的情况下改进了挑战性设想的绩效。 这种方法是不可知的 w.r.t. 。 所使用的网络结构、 投入模式或观点可以很容易地融入现有的解决方案。 代码可在 https://github.com/lmb-freib/contraspative- Future-Trajotri- pretrictionary-triction