DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction.

Predicting the trajectories of pedestrians is an important and difficult task for many applications, such as robot navigation and autonomous driving. Most of the existing methods believe that an accurate prediction of the pedestrian intention can improve the prediction quality. These works tend to predict a fixed destination coordinate as the agent intention and predict the future trajectory accordingly.

MDST-DGCN: A Multilevel Dynamic Spatiotemporal Directed Graph Convolutional Network for Pedestrian Trajectory Prediction.

Pedestrian trajectory prediction is an essential but challenging task. Social interactions between pedestrians have an immense impact on trajectories. A better way to model social interactions generally achieves a more accurate trajectory prediction.

Multicomponent Spatial-Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data.

Predicting traffic data on traffic networks is essential to transportation management. It is a challenging task due to the complicated spatial-temporal dependency. The latest studies mainly focus on capturing temporal and spatial dependencies with spatially dense traffic data.

A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing.

Most of existing traffic simulation methods have been focused on simulating vehicles on freeways or city-scale urban networks. However, relatively little research has been done to simulate intersectional traffic to date despite its broad potential applications. In this paper, we propose a novel deep learning-based framework to simulate and edit intersectional traffic.