Search Results for author: Miguel Ángel Sotelo

Found 6 papers, 1 papers with code

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

Model Guided Road Intersection Classification

no code implementations26 Apr 2021 Augusto Luis Ballardini, Álvaro Hernández, Miguel Ángel Sotelo

Understanding complex scenarios from in-vehicle cameras is essential for safely operating autonomous driving systems in densely populated areas.

Autonomous Driving Classification +1

SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs

no code implementations12 Feb 2021 Sandra Carrasco, David Fernández Llorca, Miguel Ángel Sotelo

Autonomous vehicles navigate in dynamically changing environments under a wide variety of conditions, being continuously influenced by surrounding objects.

Autonomous Vehicles Graph Attention +3

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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