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Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information
Understanding the behavior of road users is of vital importance for the development of trajectory prediction systems.
IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture
Understanding pedestrian crossing behavior is an essential goal in intelligent vehicle development, leading to an improvement in their security and traffic flow.
The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?
While driving on highways, every driver tries to be aware of the behavior of surrounding vehicles, including possible emergency braking, evasive maneuvers trying to avoid obstacles, unexpected lane changes, or other emergencies that could lead to an accident.
3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns forroad scene interpretation
Road detection and segmentation is a crucial task in computer vision for safe autonomous driving.
Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs
Different network configurations have been tested, and prediction error up to three seconds ahead is in the order of the representation resolution.
