László Palkovics

7004638104

Publications - 7

Comparison of Lateral Controllers for Autonomous Vehicles Based on Passenger Comfort Optimization

Szilárd Aradi Ádám Szabó Ákos Bokor László Palkovics

Publication Name: Proceedings of the International Conference on Informatics in Control Automation and Robotics

Publication Date: 2024-01-01

Volume: 1

Issue: Unknown

Page Range: 46-54

Description:

This paper focuses on the design of lateral controllers for autonomous vehicles. To enhance passenger comfort while concurrently maintaining minimal deviation from the desired trajectory, the developed controllers are tuned by a Genetic Algorithm, whose cost function is following the ISO 2631 Standard. Three model-based controllers, a Linear Quadratic Regulator, a Linear Quadratic Servo algorithm, and a Model Predictive Con troller have been compared in a simulation environment. The test case consists of a suburban road section, where the vehicles must successfully traverse at different velocities while minimizing the lateral acceleration and jerk affecting the passengers. To take into account the velocity-dependent dynamics of the system, the controllers are based on a Linear Parameter-Varying model of the system. The results show that the devel oped controllers meet the specified requirements regarding the equivalent acceleration, Motion Sickness Dose Value, and deviation from the desired trajectory.

Open Access: Yes

DOI: 10.5220/0012923700003822

Local Motion Planning for Overtaking Maneuvers in a Rural Road Environment

Szilárd Aradi Árpád Fehér Dániel Losonczi László Palkovics

Publication Name: Proceedings of the International Conference on Informatics in Control Automation and Robotics

Publication Date: 2024-01-01

Volume: 2

Issue: Unknown

Page Range: 220-227

Description:

This paper introduces an application of local motion planning designed explicitly for overtaking maneuvers in a rural road environment. The approach integrates multiple driving strategies for enhanced passenger comfort, including the fastest path and minimum jerk trajectory. A robust trajectory planner technique is developed using the Frenet frame, effectively considering real traffic situations, curves, and moving obstacles. Comprehensive analyses are performed on vehicle dynamics, individual cost function components, and planning and tracing times to assess the performance and computational efficiency of the proposed methods. The simulation results highlight the approach’s strengths in maintaining dynamic feasibility, ensuring safety, and enhancing passenger comfort while identifying areas for potential improvements, such as computational overhead in complex scenarios.

Open Access: Yes

DOI: 10.5220/0013001600003822

Design of Gain-Scheduled Lateral Controllers for Autonomous Vehicles †

Szilárd Aradi Ádám Szabó Ákos Bokor László Palkovics

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

This paper focuses on the design and comparative analysis of speed-dependent lateral control systems for autonomous vehicles, focusing on optimizing vehicular dynamics and passenger comfort to ensure stability and safety. Adapting control systems to varying speeds becomes crucial for maintaining stability and maneuverability as autonomous technologies progress. This study evaluates their effectiveness in real-time navigation scenarios within a simulated environment by applying gain-scheduled linear quadratic regulators and model predictive control. The results show that while traditional controllers, such as Pure Pursuit, perform adequately under constant speed conditions, adaptive model-based algorithms significantly enhance the performance, especially in dynamic driving situations involving speed variations.

Open Access: Yes

DOI: 10.3390/engproc2024079038

Expanded Applicability: Multi-Agent Reinforcement Learning-Based Traffic Signal Control in a Variable-Sized Environment

Bálint Kővári Bálint Pelenczei István Gellért Knáb László Palkovics Tamás Bécsi

Publication Name: Proceedings of the International Conference on Informatics in Control Automation and Robotics

Publication Date: 2024-01-01

Volume: 2

Issue: Unknown

Page Range: 15-25

Description:

During the development of modern cities, there is a strong demand articulated for the sustainability of progress. Since transportation is one of the main contributors to greenhouse gas emissions, the modernization and efficiency of transportation are key issues in the development of livable cities. Increasing the number of lanes does not always provide a solution and often is not feasible for various reasons. In such cases, Intelligent Transportation Systems are applied primarily in urban environments, mostly in the form of Traffic Signal Control. The majority of modern cities already employ adaptive traffic signals, but these largely utilize rule-based algorithms. Due to the stochastic nature of traffic, there arises a demand for cognitive decision-making that enables event-driven characteristics with the assistance of machine learning algorithms. While there are existing solutions utilizing Reinforcement Learning to address the problem, further advancements can be achieved in various areas. This paper presents a solution that not only reduces emissions and enhances network throughput but also ensures universal applicability regardless of network size, owing to individually tailored state representation and rewards.

Open Access: Yes

DOI: 10.5220/0012920800003822

Adaptive Highway Traffic Management: A Reinforcement Learning Approach for Variable Speed Limit Control with Random Anomalies

Bálint Kővári Bálint Pelenczei István Gellért Knáb László Palkovics Tamás Bécsi

Publication Name: Proceedings of the International Conference on Informatics in Control Automation and Robotics

Publication Date: 2024-01-01

Volume: 2

Issue: Unknown

Page Range: 117-124

Description:

Efficient traffic flow management on highway scenarios is crucial for ensuring safety and minimizing emissions through the reduction of so-called shockwave effects. In this paper, we propose a novel approach based on cooperative Multi Agent Reinforcement Learning for optimizing traffic flow, utilizing Variable Speed Limit Control in dynamic simulation environments with random anomalies. Our method leverages Reinforcement Learning to adaptively adjust speed limits on distinct road sections in response to alternating traffic conditions, thereby improving not only general traffic flow parameters, but also reducing sustainability measures overall. Through extensive simulations in a Simulation of Urban MObility environment, we demonstrate the superiority of our approach in enhancing traffic flow efficiency and robustness compared to alternative solutions found in literature. Our findings reveal an enhanced performance of RL-based VSL control over traditional approaches due to its generalizability, which contributes to the progression of Intelligent Transportation Systems by presenting a proactive and adaptable resolution for highway traffic management within dynamic real-world contexts.

Open Access: Yes

DOI: 10.5220/0012920700003822

On modeling and identification of empirical partially intelligible white noise processes

András Rövid P. Várlaki László Palkovics

Publication Name: Asian Journal of Control

Publication Date: 2021-05-01

Volume: 23

Issue: 3

Page Range: 1262-1279

Description:

The paper discusses the identification of the empirical, partially intelligible white noise processes generated by deterministic numerical algorithms. The introduced fuzzy-random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of autocorrelated white noise processes change as the order of autocorrelation increases. Based on this approach, the original empirical white noise process transformed by the autocorrelation operator can be considered to be random data series (randomlikeness), and at the same time, it has function-like characteristics (functionlikeness), as well. We approach the analysis of the mentioned complementarity by modeling the autocorrelation functions of the empirical white noise processes using tensor product (TP) model transformation.

Open Access: Yes

DOI: 10.1002/asjc.2470

Empirical white noise processes and the subjective probabilistic approaches

András Rövid P. Várlaki László Palkovics

Publication Name: Periodica Polytechnica Transportation Engineering

Publication Date: 2019-11-15

Volume: 48

Issue: 1

Page Range: 19-30

Description:

The paper discusses the identification of the empirical white noise processes generated by deterministic numerical algorithms. The introduced fuzzy-random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of auto-correlated white noise processes change as the order of autocorrelation increases. Although in this paper we rely on random number generators to get approximate white noise processes, in our upcoming research we are planning to turn the focus on physical white noise processes in order to validate our hypothesis.

Open Access: Yes

DOI: 10.3311/PPtr.15165