This repository contains algorithms for driver model analysis and prototypes for ADAS functions endowed with human-like features.
The most up-to-date versions of exact functional solutions can be found in the 'Solutions' subfolder. This contains further sub-folders with major solutions in the chain of the automated driving system. The code contained by these folders, or the links to other code spaces are validated, their validation details can be found in their corresponding read-me files. Further code can be found in the matlab_evaluation and the python_evaluation subfolders. These contain a more complex development chain, the code inside is still under development.
[1] Ignéczi G., Horvath E., Pup D.: Implementation of a self-developed model predictive control scheme for vehicle parking maneuvers. The first ISTRC Annual Conference, Tel-Aviv, Izrael, 21, June 2021.
https://www.researchgate.net/publication/354774900_Implementation_of_a_self-developed_model_predictive_control_scheme_for_vehicle_parking_maneuvers
[2] G. Igneczi and E. Horvath: A Clothoid-base Local Trajectory Planner with Extended Kalman Filter. Paper presented at the Applied Machine Intelligence and Informatics (SAMI 2022) conference, Poprád, Slovakia, 2-5 March 2022
https://ieeexplore.ieee.org/document/9780857
[3] G. Igneczi: Lane Keep Assist Optimized to Human Like Behavior. - Digital Vehicle Industry Researches at the University of Gyor, Gyor, Hungary, 15-17 June, 2022.
https://jkk-web.sze.hu/esemeny/digitalis-jarmuipari-kutatasok-2022-mesterseges-intelligencia-a-mobilitasban
[4] G. Igneczi: Validation of a Driver Model in a Closed-loop Simulation Environment - Conference of Mobility and Environment, Gyor, Hungary, 2-4. November, 2022.
https://jkk-web.sze.hu/jovoformalo-jarmuipari-kutatasok-konferencia-2022/
[5] G. Igneczi and E. Horvath: Node Point Optimization for Local Trajectory Planners based on Human Preferences. IEEE 21st World Symposium on Applied Machine Intelligence and Informatics, Herlany, Slovakia, 19-21. January, 2023.
https://ieeexplore.ieee.org/document/10044488
[6] Igneczi, G., Horvath, E., Toth, R., & Nyilas, K. (2023). Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles. Automotive Innovations - vol. 7, pp 59-70, 2024, doi: https://doi.org/10.1007/s42154-023-00259-8
https://link.springer.com/article/10.1007/s42154-023-00259-8
[7] G. Igneczi, T. Dobay: Typification of Driver Models Using Clustering Methods - Conference of Sustainable Vehicle Industry Technologies at University of Gyor, Gyor, Hungary, 14-16, June, 2023.
https://jkk-web.sze.hu/esemeny/fenntarthato-jarmuipari-technologiak-kutatasa-a-szechenyi-istvan-egyetemen-konferencia/
[8] G. Igneczi, E. Horvath and K. Nyilas: A Linear Driver Model of Local Path Planning for Lane Driving, In the proceedings of IEEE 21st International Symposium on Intelligent Systems and Informatics (SISY 2023), pp. 103-108, Pula, Croatia, 21-23, September, 2023. doi: 10.1109/SISY60376.2023.10417953
https://ieeexplore.ieee.org/document/10417953\
[9] G. Igneczi, D. Jozsa: The Implementation of a Human-Like Lane Keep System in a Prototype Vehicle, Conference of Sustainable Vehicle Industry Technologies at University of Gyor, Gyor, Hungary, 2-4, November, 2023.
https://jkk-web.sze.hu/fenntarthato-jarmuipari-technologiak-kutatasa-a-szechenyi-istvan-egyetemen-konferencia-2023-november-06-07/
[11] G. Igneczi, E. Horvath and A. Borsos: Analysis of Drivers’ Path Follow Behaviour, In the proceedings of 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO), 18-20 November, 2024, Porto, Protugal, in press
[12] G. Igneczi, E. Horvath, T. Dobay and K. Nyilas: Driver Clustering based on Individual Curve Path Selection Preferences, Automotive Innovations, in review
[13] G. Igneczi, E. Horvath: Human-Like Behaviour for Automated Vehicles (HLB4AV) Naturalistic Driving Dataset, In the proceedings of IEEE 22nd International Symposium on Intelligent Systems and Informatics (SISY 2024), in press, Pula, Croatia, 18-21, September, 2024
[14] G. Igneczi, E. Horvath, K. Nyilas: Model Predictive Planning and Control for Naturalistic Automated Driving, in review
[15] G. Igneczi, R. Toth, E. Horvath, K. Nyilas: Modelling Human Lane Following Behaviour, in review
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[V1] Romain Pepy, Alain Lambert and Hugues Mounier: Path Planning using a Dynamic Vehicle Model, Institut d'Electronique Fondamentale UMR CNRS 8622 - Universite Paris-Sud XI Bat. 220, 914U05 Orsay, France
Gergo Ferenc Igneczi has graduated as Control Engineer at Budapest University of Technology and Economics and learnt master studies in Vehicle Engineering at Széchenyi Istvan University of Győr, Hungary. He is a PhD student since 2021. As the part of his research, he studies the motion planning and control strategies of human drivers. Also, he is an active software engineer at Robert Bosch Kft, Budapest, Hungary since 2016, developing driver models and human-like motion planners and controllers for Advanced Driver Assistance Systems.
