论文标题
使用上下文增强的变压器网络的行人轨迹预测
Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks
论文作者
论文摘要
预测在共享城市交通环境中行人的轨迹仍然被认为是自动驾驶汽车（AVS）发展的挑战性问题之一。在文献中，通常使用经常性神经网络（RNN）解决此问题。尽管RNN在捕获行人运动轨迹的时间依赖性方面具有强大的功能，但在处理更长的顺序数据时，它们被认为是受到挑战的。因此，在这项工作中，我们引入了一个基于变压器网络的框架，这些框架最近被证明在许多基于顺序的任务中表现更有效，更优于RNN。我们依靠过去的位置信息的融合，代理交互信息和场景物理语义信息作为我们框架的输入，以提供对行人的强大轨迹预测。我们已经在共享城市交通环境中的两个实际行人数据集上评估了我们的框架，并且在短期和长期预测范围内，它都优于比较基线方法。
Forecasting the trajectory of pedestrians in shared urban traffic environments is still considered one of the challenging problems facing the development of autonomous vehicles (AVs). In the literature, this problem is often tackled using recurrent neural networks (RNNs). Despite the powerful capabilities of RNNs in capturing the temporal dependency in the pedestrians' motion trajectories, they were argued to be challenged when dealing with longer sequential data. Thus, in this work, we are introducing a framework based on the transformer networks that were shown recently to be more efficient and outperformed RNNs in many sequential-based tasks. We relied on a fusion of the past positional information, agent interactions information and scene physical semantics information as an input to our framework in order to provide a robust trajectory prediction of pedestrians. We have evaluated our framework on two real-life datasets of pedestrians in shared urban traffic environments and it has outperformed the compared baseline approaches in both short-term and long-term prediction horizons.
