Predicting the states of dynamic traffic actors into the fu-ture 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 sce-narios at the long tail of the dataset distribution. Our anal-ysis shows that the common losses tend to place challeng-ing cases sub-optimally in the embedding space. As a con-sequence, we propose to supplement the usual loss with aloss that places challenging cases closer to each other. Thistriggers sharing information among challenging cases andlearning specific predictive features. We show on four pub-lic datasets that this leads to improved performance on thechallenging scenarios while the overall performance staysstable. The approach is agnostic w.r.t. the used networkarchitecture, input modality or viewpoint, and can be inte-grated into existing solutions easily.
翻译:预测动态交通行为者进入未来阶段的状态对于自主系统安全高效运行非常重要。 值得注意的是, 最关键的假设情景比非关键分子少得多,更复杂。 因此, 预测中以非关键案例为主。 在本文中, 我们具体处理数据集分布长尾的具有挑战性的典型案例。 我们的肛交显示, 常见的损失往往使嵌入空间中的立体案件处于不尽人意的次要位置。 作为一种后果, 我们提议用一些损失来补充通常的损失, 这些损失使得案件彼此更接近于挑战。 这触发了挑战性案例之间的信息共享, 并学习了具体的预测特征。 我们在四套公用数据集上显示, 这导致在总体性能保持稳定的情况下改进了质疑性假设情景的性能。 这种方法是可知性 w.r.t. 。 所使用的网络结构、 输入模式或观点是可轻易被引入现有解决方案的。