Publications (7)
IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture
J. Lorenzo, I. Parra, M. A. Sotelo
Understanding pedestrian crossing behavior is an essential goal in intelligent vehicle development, leading to an improvement in their security and traffic flow. In this paper, we…
Gegenbauer and other planar orthogonal polynomials on an ellipse in the complex plane
G. Akemann, T. Nagao, I. Parra +1
We show that several families of classical orthogonal polynomials on the real line are also orthogonal on the interior of an ellipse in the complex plane, subject to a weighted pla…
Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information
A. Quintanar, D. Fernández-Llorca, I. Parra +2
Understanding the behavior of road users is of vital importance for the development of trajectory prediction systems. In this context, the latest advances have focused on recurrent…
3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns forroad scene interpretation
A. Hernández, S. Woo, H. Corrales +4
Road detection and segmentation is a crucial task in computer vision for safe autonomous driving. With this in mind, a new net architecture (3D-DEEP) and its end-to-end training me…
Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs
R. Izquierdo, A. Quintanar, I. Parra +2
This paper describes a novel approach to perform vehicle trajectory predictions employing graphic representations. The vehicles are represented using Gaussian distributions into a…
The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?
A. Quintanar, R. Izquierdo, I. Parra +2
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…