Iván García Daza received the MSc and PhD degrees in Telecommunications Engineering from the University of Alcalá (UAH), Madrid (Spain), in 2004 and 2011 respectively. At present he is Assistant Professor at the Computer Engineering Department at the University of Alcalá and member of the INVETT research group since 2007. In this period he has collaborated on more than 20 projects with public and private funding. All the projects are related with computer science techniques applied on Intelligent Transportation System.
His professional experience in the private field is 10 years, where he acquires wide knowledge about management of national and international technological projects. He developed flight control systems which were applied to Airbus A400M aircraft and optical gyro stabilization systems for surveillance with accuracy less than 1mrad based on optimal control and bayesian filter theory. He learnt methodologies to manage the development of technological projects where the number of participants is hight and variable, as us Scrum, guaranteeing the successful project development.
Its scientific production includes 11 JCR indexed publications, 9 in the first and second quartile, and 11 publications in international congresses. He has participated as a speaker in several scientific conferences and is a regular reviewer of the "IEEE Conference on Intelligent Transportation Systems" and "IEEE Conference on Intelligent Vehicles" since 2011. He was Guest Editor of Special Issue "Intelligent Vehicles", "Sensors Technologies for Intelligent Transportation Systems" and "Advanced Sensing Techniques for Autonomous Vehicles and Advanced Driver Assistance System" in Sensor an Open Access Journal. He has served as a member of the Program Committee of the 15th International Conference on Informatics in Control, Automation and Robotics held in Porto Portugal from 29 to 31 of July 2018. The number of citations to research publications evaluated by Web of Science is 128, with an average of citations per article of 7.53 points and an H index of 6.
His research interests are mainly focused on intelligent transportation systems where topics like accurate mapping process, dynamic and cinematic cars models, sensor fusion technics, pure LiDAR odometry or optimal controller theory are well known. On the docent management scope, he collaborates on Innovation Teaching Group within Systems Engineering and Automation Are, he was the Head of the Systems Engineering and Automation Teaching Area for two years, from 2018 to 2020. At present he is deputy director of Automatic Department since the mouth of January 2020. He belong to multiples Docent Comisions lie the Institution, where participate actively to address it with success.
In 2010 and 2019 he was visitor research in the Department of Applied Mechanics at Chalmers University of Technology, Göteborg. He was under the supervision of Dr Mattias Wahde in 2010, improving the knowledges about optimization algorithms that were applied in Drowsiness Detection System based on Computer Vision algorithms. He was under the supervision of Dr Ola Benderious in 2019, who participates actively with Volvo company on ITS research projects. The collaboration was about how to build a high-resolution 3D map with only LiDAR-based odometry algorithms. The collaboration allowed the scientific article "Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles".
D.Sc. on Intelligent Systems, 2011
University of Alcalá
M.Sc. in Telecommunication, 2007
University of Alcalá
Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures require driver intervention when functional system failures or critical sensor operations take place, presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre without driver mediation. All odometry systems have drift error, making it difficult to use them for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR odometry system with a fail-aware indicator. This indicator estimates a time window in which the system manages the localisation tasks appropriately. The odometry error is minimised by applying a dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the proposed method is twelfth, considering only LiDAR-based methods, where its translation and rotation errors are 1.00% and 0.0041 deg/m, respectively. Second, the encouraging results of the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results depict that, in order to achieve an accurate odometry system, complex models and measurement fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner.