Gergo Igneczi

57274975100

Publications - 13

Driver Clustering Based on Individual Curve Path Selection Preference

Ernö Horváth Gergo Igneczi Krisztian Nyilas Tamas Dobay

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-07-01

Volume: 15

Issue: 14

Page Range: Unknown

Description:

The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a full user experience. Therefore, driver modeling is a key area of research for next-generation ADASs. One of the most common tasks in everyday driving is lane keeping. Drivers are assisted by lane-keeping systems to keep their vehicle in the center of the lane. However, human drivers often deviate from the center line. It has been shown that the driver’s choice to deviate from the center line can be modeled by a linear combination of preview curvature information. This model is called the Linear Driver Model. In this paper, we fit the LDM parameters to real driving data. The drivers are then clustered based on the individual parameters. It is shown that clusters are not only formed by the numerical similarity of the driver parameters, but the drivers in a cluster actually have similar behavior in terms of path selection. Finally, an Extended Kalman Filter (EKF) is proposed to learn the model parameters at run-time. Any new driver can be classified into one of the driver type groups. This information can be used to modify the behavior of the lane-keeping system to mimic human driving, resulting in a more personalized driving experience.

Open Access: Yes

DOI: 10.3390/app15147718

Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles

R. Toth 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

Teaching Aspects of ROS 2 and Autonomous Vehicles †

Ernö Horváth Miklos Unger Rudolf Krecht Gergo Igneczi Norbert Marko

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

The advancement of autonomous vehicles (AVs) has brought forth a substantial need for effective education in robotic operating systems, particularly ROS 2, which serves as the backbone for many autonomous vehicle (AV) applications. This paper explores the academic approach and instructional methodologies tailored for teaching ROS 2 in the context of autonomous vehicle technology. It highlights the critical components and architecture of ROS 2, emphasizing its modularity, real-time communication capabilities, and robust ecosystem which make it ideal for AV development. Through a detailed curriculum outline, we describe hands-on learning activities, simulation-based exercises, and project-driven modules that facilitate deep understanding and practical skills acquisition. The effectiveness of these teaching methods is evaluated through a mixed-methods study involving student feedback, performance assessments, and project outcomes. Results indicate significant improvements in student comprehension and proficiency in both ROS 2 and autonomous vehicle systems. This research contributes to the body of knowledge by providing a comprehensive framework for educators to effectively teach ROS 2, thereby fostering the next generation of engineers proficient in developing and deploying autonomous vehicle technologies.

Open Access: Yes

DOI: 10.3390/engproc2024079049

Review of Vehicle Motion Planning and Control Techniques to Reproduce Human-like Curve-Driving Behavior †

Ernö Horváth Gergo Igneczi

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

Among the many technological challenges of automated driving development, there is an increasing focus on the behavior of these systems. Behavior is usually associated with multiple layers of control. In this paper, we focus on motion planning and control, and how these layers can be tailored to produce different behavior. Our review aims to collect and judge the most used techniques in the field of path planning and control. It has been revealed that model predictive planning and control provides high flexibility, with the cost of high computational capacity. There are simpler algorithms, such as pure-pursuit and Stanley controllers, however, these have very few parameters, therefore, the number of possible behavior patterns is limited.

Open Access: Yes

DOI: 10.3390/engproc2024079020

Human-Like Behaviour for Automated Vehicles (HLB4AV) Naturalistic Driving Dataset

Ernö Horváth Gergo Igneczi

Publication Name: Sisy 2024 IEEE 22nd International Symposium on Intelligent Systems and Informatics Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 491-496

Description:

Human-Like Behaviour for Automated Vehicles (HLB4AV) dataset is a collection of data of drivers, driving naturally in a rural road segment. The instructions were always to drive with their own, instinctive style, without knowing the goal of the measurement. The measurements took place always during the day, on weekdays, with no or low traffic, good sight visibilities and dry weather conditions. There are always reference measurements of the given road sections, where professional drivers were asked to drive a well-sensored test vehicle. Dataset contains information of accurate vehicle kinematics, lane edge geometry, test vehicle localization and surrounding traffic. The dataset is unique as besides physical vehicle data metadata of the drivers in terms of driving frequency, experience and age is also added. The data can be used to analize the basic path and speed selection of drivers on rural roads, their reaction on other traffic participants or lane offset selection. The data continuously grows, new measurement platforms as well as new drivers and road sections are planned to be added.

Open Access: Yes

DOI: 10.1109/SISY62279.2024.10737549

Innovative Cone Clustering and Path Planning for Autonomous Formula Student Race Cars Using Cameras †

Gergo Igneczi Balázs Szőnyi

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

In this research, we present a novel approach for cone clustering, path planning, and path visualization in autonomous Formula Student race cars, utilizing the YOLOv8 model and a ZED 2 camera, executed on a Jetson Orin computer. Our system first identifies and then deprojects the positions of cones in space, employing an advanced clustering mechanism to generate midpoints and draw connecting lines. In previous clustering algorithms, cones were stored separately by color and connected based on relevance to create the lane edges. However, our proposed solution adopts a fundamentally different approach. Cones on the left and right sides within a dynamically changing maximum and minimum distance are connected by a central line, and the midpoint of this line is marked distinctly. Cones connected in this manner are then linked by their positions to form the edges of the track. The midpoints on these central lines are displayed as markers, facilitating the visualization of the optimal path. In our research, we also cover the analysis of the clustering algorithm on global maps. The implementation utilizes the ROS 2 framework for real-time data handling and visualization. Our results demonstrate the system’s efficiency in dynamic environments, highlighting potential advancements in the field of autonomous racing. The limitation of our approach is the dependency on precise cone detection and classification, which may be affected by environmental factors such as lighting and cone positioning.

Open Access: Yes

DOI: 10.3390/engproc2024079096

Node Point Optimization for Local Trajectory Planners based on Human Preferences

Ernö Horváth Gergo Igneczi

Publication Name: 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics Sami 2023 Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: 225-230

Description:

There is an increased number of Driver Assistance systems on the field, therefore the need of having naturalistic behavior of these functions is increasing. In our work the trajectory planning task is analyzed. A clothoid-based local trajectory planning algorithm is proposed, which relies on node points within the look ahead distance. The node point distances were optimized to yield a global trajectory which is close to the human drivers' path. Real driving data was used as the optimization reference. As a result of the optimization, we were able to determine a characteristic node point distance set which fits all drivers. We have also shown that three node points within a look ahead distance of 140 m are sufficient to describe the drivers' trajectory. Later this result will serve as a basis to build a driver model which calculates the lateral coordinates of the node points.

Open Access: Yes

DOI: 10.1109/SAMI58000.2023.10044488

A Linear Driver Model of Local Path Planning for Lane Driving

Ernö Horváth Gergo Igneczi Krisztian Nyilas

Publication Name: Sisy 2023 IEEE 21st International Symposium on Intelligent Systems and Informatics Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: 103-108

Description:

Modern lane centering assistance systems often use the mid-lane as a reference line for tracking. However, human drivers prefer to keep an offset to the mid-lane. Therefore, we have proposed in our previous work a driver model which calculates human-like offset values and enable the path planner to plan human-like paths. The driving data of 15 drivers have been analyzed. It has been revealed that drivers behave differently in left or right curves and choose to drive on either the left or the right side in straight road sections. Therefore, we extend our model to handle these traits. We have shown that the extended model outperforms the previous symmetric model.

Open Access: Yes

DOI: 10.1109/SISY60376.2023.10417953

A Clothoid-based Local Trajectory Planner with Extended Kalman Filter

Ernö Horváth Gergo Igneczi

Publication Name: Sami 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics Proceedings

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: 467-472

Description:

The paper introduces a local trajectory planner designed specifically for lateral guidance of autonomous vehicles. The inputs of the planner are the lane edges in the form of corner point coordinates in a two-dimensional plane. The aim of the planner is to provide a series of trajectory points ahead of the vehicle. The trajectory shall be well-conditioned which means no border violation (safety), no high lateral acceleration (comfort) and good tracking properties. The optimal conditions for driving have been found in using clothoid curves. The curvature of the clothoid is a linear function of the distance, which resolves the biggest disadvantage of circle conjunction: the discontinuity of the lateral acceleration. Clothoids have constant lateral jerk profile. In our work an Extended Kalman Filter is used with a clothoid model to consolidate inaccuracies of the lane detection system. The paper is presented as the first part of a research process. The algorithm introduced in this paper is planned to be continued with research on its automatized calibration procedures.

Open Access: Yes

DOI: 10.1109/SAMI54271.2022.9780857

Modeling Human Lane Following Behavior

R. Toth Ernö Horváth Gergo Igneczi Krisztian Nyilas

Publication Name: IEEE Access

Publication Date: 2025-12-19

Volume: 13

Issue: Unknown

Page Range: 214940-214959

Description:

Vehicles with driver assistance and even autonomous driving capabilities have become widely spread on the roads in the last decades. Replacing human drivers even partially is a complex issue, as these driving systems must be safe and reliable under various conditions. The complexity is increased further by the fact that driving systems must interact with human participants of the traffic: the driver and passengers of the given vehicle as well as drivers in other vehicles or pedestrians. The market trends show that even though more and more driving assistance solutions are available, the vehicle users often refuse to use them as the behavior of these systems feels unnatural. Therefore, manufacturers have initiated the development of personalized driving assistance which motivates research in driver modeling and driving style classification. One of the most controversial assistance function is lane keeping. In our paper, we propose various different model structures that are able to capture the lane offset selection behavior of human drivers. It is shown that a static linear regression model provides reasonable accuracy and robustness in modeling the lane offset. While Gaussian Process models offer more accuracy, their training time is more demanding. It is shown, that a combination of linear and polynomial third-order basis functions offers a good tradeoff to efficiently describe the lane offset selection of human drivers. Our observations are based on studying real driving data from 41 drivers, whose behavior is modeled with the considered model structures. Then, using the corresponding model parameters, drivers are clustered into two driving style groups. Using the collective data of these groups the models are fitted to all data records in the two driver groups. Any new driver can be classified into one of these groups, and the aggregated models can be further personalized to to increase user satisfaction with the lane keeping systems.

Open Access: Yes

DOI: 10.1109/ACCESS.2025.3646260

Spectral Analysis of the Lateral Dynamics of Road Vehicles †

David Jozsa Dániel Pup György Istenes Gergo Igneczi József Bokor

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

In this paper, a time domain and a spectral analysis of the lateral dynamics of a Lexus passenger car are presented. Measurements were made of the vehicle’s lateral acceleration and steering angle. The aim of the measurements is to understand the vehicle’s lateral dynamics during different cornering maneuvers. For this purpose, part of the measurements is performed with a driver and the other part with autonomous control. The data processed and analyzed in this research can be used to determine the nature of the lateral dynamics, which is essential to establishing a mathematical relationship between the measured signals. This will allow the identification and modeling of vehicle dynamics, which is key to the development and optimization of autonomous vehicle control systems.

Open Access: Yes

DOI: 10.3390/engproc2025113063

Lightweight Solution to Generate Accurate Lanelet Maps †

David Jozsa Gergo Igneczi Matyas Mesics

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

As automated driving technologies become more mature, there is an increasing reliance on digital maps to support safe and efficient driving. Sensors like cameras and radars can be limited by occlusions, lighting conditions, or weather, and often fall short. High-definition (HD) maps offer excellent accuracy, but they are expensive to produce. These limitations make these techniques impractical for large-scale deployment. What makes our approach particularly attractive is its hardware simplicity: the entire process requires only a precise GNSS receiver and a commonly available lane detection camera, eliminating the need for expensive sensors like LiDAR or complex multi-vehicle fleets. We rigorously evaluated our method in a highway environment, where a vehicle equipped with our generated maps successfully executed autonomous lane following and adapted its speed based on detected speed limit signs. The positional deviation of the resulting maps was consistently under 5 cm.

Open Access: Yes

DOI: 10.3390/engproc2025113068

Model Predictive Planning and Control for Naturalistic Automated Driving

Ernö Horváth Gergo Igneczi Krisztian Nyilas

Publication Name: Cinti 2025 IEEE 25th International Symposium on Computational Intelligence and Informatics Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 45-50

Description:

The evolution of Advanced Driver Assistance Systems (ADAS) has reached a stage of maturity where the social acceptance of these systems is becoming as important as coping with technical challenges. Most users complain that ADAS functions behave unnaturally, and they feel anxious because the driving system behaves in a way that is far from what the user prefers. One of the most controversial ADAS functions is Lane Keeping Assistance (LKA). This system detects the edge of the lane and drives the car actively by intervening on the steering wheel. Most available lane keeping follows the center of the lane, while the vehicle controller oscillates and often deviates from this line. Most people find this operation disturbing and unreliable. In our work, we propose a comprehensive Model Predictive Planner and Control that preserves the basic concept of how human drivers drive: they instinctively create the combined representation of the environment and the vehicle, and predictevely plan their action trajectory (i.e., steering angle). This method does not divide the driving task into planning and control, thereby improving tunability. On the other hand, the method provides direct relation to vehicle dynamics and high-level policies, and therefore it can be validated easier than end-to-end trained functions, such as neural networks. The initial algorithm is implemented in MATLAB and validated via simulation. The test script and the data can be accessed upon request11https://github.com/jkk-research/jkk_controllers/tree/prototype/mppc

Open Access: Yes

DOI: 10.1109/CINTI67731.2025.11311824