R. Tóth

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Publications - 5

Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles

R. Tóth Ernö Horváth Gergo Igneczi Krisztian Nyilas

Publication Name: Automotive Innovation

Publication Date: 2024-02-01

Volume: 7

Issue: 1

Page Range: 59-70

Description:

Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.

Open Access: Yes

DOI: 10.1007/s42154-023-00259-8

Backflipping With Miniature Quadcopters by Gaussian-Process-Based Control and Planning

R. Tóth Tamas Peni Peter Antal

Publication Name: IEEE Transactions on Control Systems Technology

Publication Date: 2024-01-01

Volume: 32

Issue: 1

Page Range: 3-14

Description:

This article proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian process (GP) model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first, a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties. The controller is constructed by augmenting the nominal model of the drone with a GP that is trained using measurement data. Second, an efficient trajectory planning algorithm is proposed, which designs feasible trajectories for the flip maneuver using only quadratic programming. The two approaches are analyzed in simulations and in real experiments using Bitcraze Crazyflie 2.1 quadcopters.

Open Access: Yes

DOI: 10.1109/TCST.2023.3297744

Learning Stable and Robust Linear Parameter-Varying State-Space Models

R. Tóth Ruigang Wang Chris Verhoek

Publication Name: Proceedings of the IEEE Conference on Decision and Control

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: 1348-1353

Description:

This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value γ. Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem.

Open Access: Yes

DOI: 10.1109/CDC49753.2023.10384260

Data-driven linear parameter-varying modelling of the steering dynamics of an autonomous car

Gabor Rodonyi R. Tóth Peter Koros Dániel Pup Ádám Kisari Zsombor Vigh J. Bokor

Publication Name: IFAC Papersonline

Publication Date: 2021-07-01

Volume: 54

Issue: 8

Page Range: 20-26

Description:

Developing automatic driving solutions and driver support systems requires accurate vehicle specific models to describe and predict the associated motion dynamics of the vehicle. Despite of the mature understanding of ideal vehicle dynamics, which are inherently nonlinear, modern cars are equipped with a wide array of digital and mechatronic components that are difficult to model. Furthermore, due to manufacturing, each car has its personal motion characteristics which change over time. Hence, it is important to develop data-driven modelling methods that are capable to capture from data all relevant aspects of vehicle dynamics in a model that is directly utilisable for control. In this paper, we show how Linear Parameter-Varying (LPV) modelling and system identification can be applied to reliably capture personalised model of the steering system of an autonomous car based on measured data. Compared to other nonlinear identification techniques, the obtained LPV model is directly utilisable for powerful controller synthesis methods of the LPV framework.

Open Access: Yes

DOI: 10.1016/j.ifacol.2021.08.575

Identification of the nonlinear steering dynamics of an autonomous vehicle

Gabor Rodonyi R. Tóth M. Schoukens Peter Koros Dániel Pup Ádám Kisari G. I. Beintema Zsombor Vigh Alexandros Soumelidis J. Bokor

Publication Name: IFAC Papersonline

Publication Date: 2021-07-01

Volume: 54

Issue: 7

Page Range: 708-713

Description:

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results.

Open Access: Yes

DOI: 10.1016/j.ifacol.2021.08.444