A dual learning model for vehicle trajectory prediction
M Khakzar, A Rakotonirainy, A Bond… - IEEE Access, 2020 - ieeexplore.ieee.org
Automated vehicles and advanced driver-assistance systems require an accurate prediction
of future traffic scene states. The tendency in recent years has been to use deep learning …
of future traffic scene states. The tendency in recent years has been to use deep learning …
Tpnet: Trajectory proposal network for motion prediction
Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
Vehicle trajectory prediction at intersections using interaction based generative adversarial networks
Vehicle trajectory prediction at intersections is both essential and challenging for
autonomous vehicle navigation. This problem is aggravated when the traffic is …
autonomous vehicle navigation. This problem is aggravated when the traffic is …
Ast-gnn: An attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction
Predicting pedestrian trajectories in the future is a basic research topic in many real
applications, such as video surveillance, self-driving cars, and robotic systems. There are …
applications, such as video surveillance, self-driving cars, and robotic systems. There are …
Spatiotemporal attention-based graph convolution network for segment-level traffic prediction
Traffic prediction, as a core component of intelligent transportation systems (ITS), has been
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …