3 citations · 3 across the 6 of their papers we have counts for
6 papers
The LAIA Dataset: Labelled Attention for Intelligent Automobiles
A. Contreras, D. Porres, R. Abad +5
The development of autonomous vehicles (AVs) usually relies heavily on data-driven artificial intelligence (AI) models that require large volumes of sensor data with ground-truth a…
Multimodal Action Diffusion for Robust End-to-End Autonomous Driving
Jorge Daniel Rodríguez-Vidal, Diego Porres, Gabriel Villalonga Pineda +1
End-to-End Autonomous Driving (E2E-AD) systems have largely converged on predicting intermediate trajectory waypoints, delegating final control to hand-crafted controllers with GPS…
Addressing the Waypoint-Action Gap in End-to-End Autonomous Driving via Vehicle Motion Models
Jorge Daniel Rodríguez-Vidal, Gabriel Villalonga, Diego Porres +1
End-to-End Autonomous Driving (E2E-AD) systems are typically grouped by the nature of their outputs: (i) waypoint-based models that predict a future trajectory, and (ii) action-bas…
Guiding Attention in End-to-End Driving Models
Diego Porres, Yi Xiao, Gabriel Villalonga +2
Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. However, training these well-performing models usually…
Scaling Vision-based End-to-End Driving with Multi-View Attention Learning
Yi Xiao, Felipe Codevilla, Diego Porres +1
On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., st…
Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks
Diego Porres
Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and st…