Human motion prediction is key to understand social environments, with direct
applications in robotics, surveillance, etc. We present a simple yet effective
pedestrian trajectory prediction model aimed at pedestrians positions
prediction in urban-like environments conditioned by the environment: map and
surround agents. Our model is a neural-based architecture that can run several
layers of attention blocks and transformers in an iterative sequential fashion,
allowing to capture the important features in the environment that improve
prediction. We show that without explicit introduction of social masks,
dynamical models, social pooling layers, or complicated graph-like structures,
it is possible to produce on par results with SoTA models, which makes our
approach easily extendable and configurable, depending on the data available.
We report results performing similarly with SoTA models on publicly available
and extensible-used datasets with unimodal prediction metrics ADE and FDE.

本文通过利用神经网络的注意力机制和迭代逐步学习的方式，结合环境因素预测市区环境下行人位置的运动，成功构建了一个简单、高效的行人轨迹预测模型，并证明了该模型在不需要引入实体掩码、动态模型、社交池层或类似图形结构方面，也能够与 SoTA 模型相媲美地达到相似的性能水平。

使用迭代式注意力块的受条件控制的人类轨迹预测

Conditioned Human Trajectory Prediction using Iterative Attention Blocks

Reliable anticipation of pedestrian trajectory is imperative for the
operation of autonomous vehicles and can significantly enhance the
functionality of advanced driver assistance systems. While significant progress
has been made in the field of pedestrian detection, forecasting pedestrian
trajectories remains a challenging problem due to the unpredictable nature of
pedestrians and the huge space of potentially useful features. In this work, we
present a deep learning approach for pedestrian trajectory forecasting using a
single vehicle-mounted camera. Deep learning models that have revolutionized
other areas in computer vision have seen limited application to trajectory
forecasting, in part due to the lack of richly annotated training data. We
address the lack of training data by introducing a scalable machine annotation
scheme that enables our model to be trained using a large dataset without human
annotation. In addition, we propose Dynamic Trajectory Predictor (DTP), a model
for forecasting pedestrian trajectory up to one second into the future. DTP is
trained using both human and machine-annotated data, and anticipates dynamic
motion that is not captured by linear models. Experimental evaluation confirms
the benefits of the proposed model.

本篇论文提出使用单个车载摄像头的深度学习方法预测行人轨迹，通过引入可扩展的机器注释方案来解决训练数据不足的问题，并提出了动态轨迹预测器（DTP）来预测未来一秒的行人轨迹，实验证实了该模型的好处。