论文标题
通过转移半监督模型的转移学习的车辆轨迹预测
Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models
论文作者
论文摘要
在这项工作中，我们表明，用于车辆轨迹预测的半监督模型可显着提高对最先进的现实基准测试的监督模型的性能。从监督到半监督模型可以通过使用未标记的数据来扩展，从而将预训练中图像数量从数百万增加到十亿。我们进行消融研究，比较半监督和监督模型的转移学习，同时使所有其他因素保持平等。在半监督模型中，我们将对比学习与教师研究方法以及网络进行比较，以及通过网络预测少量轨迹的网络，网络可以预测大型轨迹集的概率。我们使用驾驶环境的低级和中级表示的结果证明了半监督方法在现实世界轨迹预测中的适用性。
In this work we show that semi-supervised models for vehicle trajectory prediction significantly improve performance over supervised models on state-of-the-art real-world benchmarks. Moving from supervised to semi-supervised models allows scaling-up by using unlabeled data, increasing the number of images in pre-training from Millions to a Billion. We perform ablation studies comparing transfer learning of semi-supervised and supervised models while keeping all other factors equal. Within semi-supervised models we compare contrastive learning with teacher-student methods as well as networks predicting a small number of trajectories with networks predicting probabilities over a large trajectory set. Our results using both low-level and mid-level representations of the driving environment demonstrate the applicability of semi-supervised methods for real-world vehicle trajectory prediction.
