15 citations · 61 across the 17 of their papers we have counts for
17 papers
Realistic pedestrian behaviour in the CARLA simulator using VR and mocap
Sergio Martín Serrano, David Fernández Llorca, Iván García Daza +1
Simulations are gaining increasingly significance in the field of autonomous driving due to the demand for rapid prototyping and extensive testing. Employing physics-based simulati…
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…
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs
Sandra Carrasco Limeros, Sylwia Majchrowska, Joakim Johnander +3
Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depen…
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…
Insertion of real agents behaviors in CARLA autonomous driving simulator
Sergio Martín Serrano, David Fernández Llorca, Iván García Daza +1
The role of simulation in autonomous driving is becoming increasingly important due to the need for rapid prototyping and extensive testing. The use of physics-based simulation inv…
Testing predictive automated driving systems: lessons learned and future recommendations
Rubén Izquierdo Gonzalo, Carlota Salinas Maldonado, Javier Alonso Ruiz +3
Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels. These app…
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…
Model Guided Road Intersection Classification
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. Among these, intersection areas are…
SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs
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. Modelling interactions a…
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…
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…
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition
Raul Quintero, Ignacio Parra, David Fernandez Llorca +1
According to several reports published by worldwide organisations, thousands of pedestrians die in road accidents every year. Due to this fact, vehicular technologies have been evo…
A Method for Vehicle Collision Risk Assessment through Inferring Driver's Braking Actions in Near-Crash Situations
Liqun Peng, Miguel Angel Sotelo, Yi He +2
Driving information and data under potential vehicle crashes create opportunities for extensive real-world observations of driver behaviors and relevant factors that significantly…
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…