55 citations · 76 across the 7 of their papers we have counts for
6 papers · 1 filter
Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving
David Fernández Llorca, Pedro Frau, Ignacio Parra +2
This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal r…
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…
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…
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…
RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features
Javier Lorenzo, Ignacio Parra, Florian Wirth +3
Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a h…
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…