55 citations · 72 across the 16 of their papers we have counts for
17 papers
Decision Making and Ride Comfort Evaluation Based on Lane-Change Prediction for Autonomous Vehicles
Mohamed Manzour, Aditya Kumar, Rubén Izquierdo +1
Predicting surrounding vehicles' lane-change intentions enables autonomous vehicles to anticipate potential hazards and respond proactively rather than relying solely on reactive m…
Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction
Mohamed Manzour, Catherine M. Elias, Omar M. Shehata +2
Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre-recorded…
Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference
M. Manzour, Catherine M. Elias, Omar M. Shehata +2
Research on lane change prediction has gained a lot of momentum in the last couple of years. However, most research is confined to simulation or results obtained from datasets, lea…
Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation
M. Manzour, A. Ballardini, R. Izquierdo +1
Lane-changing maneuvers, particularly those executed abruptly or in risky situations, are a significant cause of road traffic accidents. However, current research mainly focuses on…
Behavioural gap assessment of human-vehicle interaction in real and virtual reality-based scenarios in autonomous driving
Sergio. Martín Serrano, Rubén Izquierdo, Iván García Daza +2
In the field of autonomous driving research, the use of immersive virtual reality (VR) techniques is widespread to enable a variety of studies under safe and controlled conditions.…
RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models
Mohamed Manzour Hussien, Angie Nataly Melo, Augusto Luis Ballardini +3
Prediction of road users' behaviors in the context of autonomous driving has gained considerable attention by the scientific community in the last years. Most works focus on predic…
Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario
Rubén Izquierdo, Javier Alonso, Ola Benderius +2
This study presents the outcomes of empirical investigations pertaining to human-vehicle interactions involving an autonomous vehicle equipped with both internal and external Human…
Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference
M. Manzour, A. Ballardini, R. Izquierdo +1
Prediction of vehicle lane change maneuvers has gained a lot of momentum in the last few years. Some recent works focus on predicting a vehicle's intention by predicting its trajec…
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…
Human-vehicle interaction for autonomous vehicles in crosswalk scenarios: Field experiments with pedestrians and passengers
R. Izquierdo, S. Martín, J. Alonso +3
This paper presents the results of real-world testing of human-vehicle interactions with an autonomous vehicle equipped with internal and external Human Machine Interfaces (HMIs) i…
Digital twin in virtual reality for human-vehicle interactions in the context of autonomous driving
Sergio Martín Serrano, Rubén Izquierdo, Iván García Daza +2
This paper presents the results of tests of interactions between real humans and simulated vehicles in a virtual scenario. Human activity is inserted into the virtual world via a v…
Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning
Rubén Izquierdo, Álvaro Quintanar, David Fernández Llorca +4
This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird's eye view representations and convolutional neural networks. Vehicl…
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…
WiFiNet: WiFi-based indoor localisation using CNNs
Noelia Hernández, Ignacio Parra, Héctor Corrales +4
Different technologies have been proposed to provide indoor localisation: magnetic field, bluetooth , WiFi, etc. Among them, WiFi is the one with the highest availability and highe…
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…
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…
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
Augusto Luis Ballardini, Daniele Cattaneo, Rubén Izquierdo +4
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 relyin…