Search Results for author: Rubén Izquierdo

Found 5 papers, 1 papers with code

Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving

1 code implementation11 Dec 2023 David Fernández Llorca, Pedro Frau, Ignacio Parra, Rubén Izquierdo, Emilia Gómez

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 role in the overall functioning of Autonomous Vehicles (AVs).

Attribute Autonomous Driving +1

Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario

no code implementations11 Dec 2023 Rubén Izquierdo, Javier Alonso, Ola Benderius, Miguel Ángel Sotelo, David Fernández Llorca

The internal and external HMIs were integrated with implicit communication techniques, incorporating a combination of gentle and aggressive braking maneuvers within the crosswalk.

Autonomous Vehicles

Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning

no code implementations4 Jul 2022 Rubén Izquierdo, Álvaro Quintanar, David Fernández Llorca, Iván García Daza, Noelia Hernández, Ignacio Parra, Miguel Ángel Sotelo

The U-net model has been selected as the prediction kernel to generate future visual representations of the scene using an image-to-image regression approach.

Trajectory Prediction

WiFiNet: WiFi-based indoor localisation using CNNs

no code implementations14 Apr 2021 Noelia Hernández, Ignacio Parra, Héctor Corrales, Rubén Izquierdo, Augusto Luis Ballardini, Carlota Salinas, Iván Garcia

Different technologies have been proposed to provide indoor localisation: magnetic field, bluetooth , WiFi, etc.

Transfer Learning

Vehicle Ego-Lane Estimation with Sensor Failure Modeling

no code implementations5 Feb 2020 Augusto Luis Ballardini, Daniele Cattaneo, Rubén Izquierdo, Ignacio Parra Alonso, Andrea Piazzoni, Miguel Ángel Sotelo, Domenico Giorgio Sorrenti

We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker.

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