Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and
autonomous vehicles. Existing methods mainly rely on generic trajectory predictions derived …

Traffic speed prediction is a crucial and challenging task for intelligent transportation
systems. The prediction task can be accomplished via graph neural networks with structured …

Traffic networks exhibit complex spatial-temporal dependencies, and accurately capturing
such dependencies is critical to improving prediction accuracy. Recently, many deep …

Predicting the future trajectory of surrounding vehicles is essential for the navigation of
autonomous vehicles in complex real-world driving scenarios. It is challenging as a vehicle's …

Ridesharing companies such as Ube and DiDi provide ride-hailing services where
passengers and drivers are matched via mobile apps. As a result, large amounts of vehicle …

We address the problem of traffic prediction on large-scale road networks. We propose a
novel deep learning model, Virtual Dynamic Graph Convolution Neural Network and …

As a typical problem in time series analysis, traffic flow prediction is one of the most
important application fields of machine learning. However, achieving highly accurate traffic …

Predicting the trajectories of surrounding heterogeneous traffic agents is critical for the
decision making of an autonomous vehicle. Recently, many existing prediction methods …

The trajectory prediction is a critical and challenging problem in the design of an
autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber …

For autonomous vehicles driving on roads, future trajectories of surrounding traffic agents
(eg, vehicles, bicycles, pedestrians) are essential information. The prediction of future …