Academic Positions

  • Present 2023

    PhD Grant

    INVETT Research Group (Intelligent Vehicles and Traffic Technologies), Universidad de Alcalá

  • 2018 2019

    Researcher

    INVETT Research Group (Intelligent Vehicles and Traffic Technologies), Universidad de Alcalá

Education & Training

  • PhDongoing

    PhD in Information and Communications Technologies

    Universidad de Alcalá, Spain

  • MSc.2019

    Master of Science in Telecommunications Engineering

    Universidad de Alcalá, Spain.

  • BSc.2017

    Bachelor of Science in Telecommunications Engineering

    Universidad de Alcalá

INVETT

Current Teaching

  • 2019 2021

    C/C++ Programming

    The objective of the course is the study in depth the structured programming using C programming language. The programme of the course is: review of basic concepts about pointers, advanced use of pointers, advanced management of functions, creation and manipulation of files, dynamic data structures and algorithms.

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Vehicle Lane Change Prediction on Highways Using Efficient Environment Representation and Deep Learning

Journal Paper R. Izquierdo, A. Quintanar, J. Lorenzo, I. García-Daza, I. Parra, D. Fernández-Llorca, and M. A. Sotelo, "Vehicle Lane Change Prediction on Highways Using Efficient Environment Representation and Deep Learning", IEEE Access, 2021, (JCR-IF-2020: 3.367; Q2).

Abstract

This paper introduces a novel method of lane-change and lane-keeping detection and prediction of surrounding vehicles based on Convolutional Neural Network (CNN) classification approach. Context, interaction, vehicle trajectories, and scene appearance are efficiently combined into a single RGB image that is fed as input for the classification model. Several state-of-the-art classification-CNN models of varying complexity are evaluated to find out the most suitable one in terms of anticipation and prediction. The model has been trained and evaluated using the PREVENTION dataset, a specific dataset oriented to vehicle maneuver and trajectory prediction. The proposed model can be trained and used to detect lane changes as soon as they are observed, and to predict them before the lane change maneuver is initiated. Concurrently, a study on human performance in predicting lane-change maneuvers using visual inputs has been conducted, so as to establish a solid benchmark for comparison. The empirical study reveals that humans are able to detect the 83.9% of lane changes on average 1.66 seconds in advance. The proposed automated maneuver detection model increases anticipation by 0.43 seconds and accuracy by 2.5% compared to human results, while the maneuver prediction model increases anticipation by 1.03 seconds with an accuracy decrease of only 0.5%.

Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information

Conference Paper (ArXiv) A. Quintanar, D. Fernández-Llorca, I. Parra, R. Izquierdo, and M. A. Sotelo, "Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information," accepted for publication at IEEE Intelligent Vehicles Symposium 2021, arXiv:2106.00559.

Abstract

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 structures, establishing the social interaction between the agents involved in the scene. More recently, simpler structures have also been introduced for predicting pedestrian trajectories, based on Transformer Networks, and using positional information. They allow the individual modelling of each agent's trajectory separately without any complex interaction terms. Our model exploits these simple structures by adding augmented data (position and heading), and adapting their use to the problem of vehicle trajectory prediction in urban scenarios in prediction horizons up to 5 seconds. In addition, a cross-performance analysis is performed between different types of scenarios, including highways, intersections and roundabouts, using recent datasets (inD, rounD, highD and INTERACTION). Our model achieves state-of-the-art results and proves to be flexible and adaptable to different types of urban contexts.

The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?

Conference Paper A. Quintanar, R. Izquierdo, I. Parra, D. Fernández-Llorca, and M. A. Sotelo, "The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?," 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, 2020, pp. 45-50, doi: 10.1109/IV47402.2020.9304640.

Abstract

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, unexpected lane changes, or other emergencies that could lead to an accident. In this paper, human’s ability to predict lane changes in highway scenarios is analyzed through the use of video sequences extracted from the PREVENTION dataset, a database focused on the development of research on vehicle intention and trajectory prediction. Thus, users had to indicate the moment at which they considered that a lane change maneuver was taking place in a target vehicle, subsequently indicating its direction: left or right. The results retrieved have been carefully analyzed and compared to ground truth labels, evaluating statistical models to understand whether humans can actually predict. The study has revealed that most participants are unable to anticipate lane-change maneuvers, detecting them after they have started. These results might serve as a baseline for AI’s prediction ability evaluation, grading if those systems can outperform human skills by analyzing hidden cues that seem unnoticed, improving the detection time, and even anticipating maneuvers in some cases.

Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs.

Conference Paper R. Izquierdo, A. Quintanar, I. Parra, D. Fernández-Llorca, and M. A. Sotelo, "Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs," 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 2020, pp. 1-6, doi: 10.1109/ITSC45102.2020.9294732.

Abstract

This paper describes a novel approach to perform vehicle trajectory predictions employing graphic representations. The vehicles are represented using Gaussian distributions into a Bird Eye View. Then the U-net model is used to perform sequence to sequence predictions. This deep learning-based methodology has been trained using the HighD dataset, which contains vehicles' detection in a highway scenario from aerial imagery. The problem is faced as an image to image regression problem training the network to learn the underlying relations between the traffic participants. This approach generates an estimation of the future appearance of the input scene, not trajectories or numeric positions. An extra step is conducted to extract the positions from the predicted representation with subpixel resolution. Different network configurations have been tested, and prediction error up to three seconds ahead is in the order of the representation resolution. The model has been tested in highway scenarios with more than 30 vehicles simultaneously in two opposite traffic flow streams showing good qualitative and quantitative results.

Experimental validation of lane-change intention prediction methodologies based on CNN and LSTM

Conference Paper R. Izquierdo, A. Quintanar, I. Parra, D. Fernández-Llorca, and M. A. Sotelo, "Experimental validation of lane-change intention prediction methodologies based on CNN and LSTM," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 3657-3662, doi: 10.1109/ITSC.2019.8917331.

Abstract

This paper describes preliminary results of two different methodologies used to predict lane changes of surrounding vehicles. These methodologies are deep learning based and the training procedure can be easily deployed by making use of the labeling and data provided by The PREVENTION dataset. In this case, only visual information (data collected from the cameras) is used for both methodologies. On the one hand, visual information is processed using a new multi-channel representation of the temporal information which is provided to a CNN model. On the other hand, a CNN-LSTM ensemble is also used to integrate temporal features. In both cases, the idea is to encode local and global context features as well as temporal information as the input of a CNN-based approach to perform lane change intention prediction. Preliminary results showed that the dataset proved to be highly versatile to deal with different vehicle intention prediction approaches.

The PREVENTION dataset: a novel benchmark for PREdiction of VEhicles iNTentIONs

Conference Paper R. Izquierdo, A. Quintanar, I. Parra, D. Fernández-Llorca, and M. A. Sotelo, "The PREVENTION dataset: a novel benchmark for PREdiction of VEhicles iNTentIONs," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 3114-3121, doi: 10.1109/ITSC.2019.8917433.

Abstract

Recent advances in autonomous driving have shown the importance of endowing self-driving cars with the ability of predicting the intentions and future trajectories of other traffic participants. In this paper, we introduce the PREVENTION dataset, which provides a large number of accurate and detailed annotations of vehicles trajectories, categories, lanes, and events, including cut-in, cut-out, left/right lane changes, and hazardous maneuvers. Data is collected from 6 sensors of different nature (LiDAR, radar, and cameras), providing both redundancy and complementarity, using an instrumented vehicle driven under naturalistic conditions. The dataset contains 356 minutes, corresponding to 540 km of distance traveled, including more than 4M detections, and more than 3K trajectories. Each vehicle is unequivocally identified with a unique id and the corresponding image, LiDAR and radar coordinates. No other public dataset provides such a rich amount of data on different road scenarios and critical situations and such a long-range coverage around the ego-vehicle (up to 80 m) using a redundant sensor set-up and providing enhanced lane-change annotations of surrounding vehicles. The dataset is ready to develop learning and inference algorithms for predicting vehicles intentions and future trajectories, including inter-vehicle interactions.

CNNs for Fine-Grained Car Model Classification

Conference Paper H. Corrales, D. F. Llorca, I. Parra, S. Vigre, A. Quintanar, J. Lorenzo, N. Hernández, International Conference on Computer Aided Systems Theory, 2019.

Abstract

This paper describes an end-to-end training methodology for CNN-based fine-grained vehicle model classification. The method relies exclusively on images, without using complicated architectures. No extra annotations, pose normalization or part localization are needed. Different full CNN-based models are trained and validated using CompCars [31] dataset, for a total of 431 different car models. We obtained a top-1 validation accuracy of 97.62% which substantially outperforms previous works.

Location & Address


Room E-202.
Dpto. Automatica
Escuela Politecnica. Campus Universitario.
Ctra. Madrid-Barcelona, Km. 33,600.
28805 Alcalá de Henares (Madrid), Spain