Miklos Unger

57219414984

Publications - 7

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

Inverse Perspective Mapping Correction for Aiding Camera-Based Autonomous Driving Tasks †

Peter Koros Miklos Unger Norbert Marko

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

Inverse perspective mapping (IPM) is a crucial technique in camera-based autonomous driving, transforming the perspective view captured by the camera into a bird’s-eye view. This can be beneficial for accurate environmental perception, path planning, obstacle detection, and navigation. IPM faces challenges such as distortion and inaccuracies due to varying road inclinations and intrinsic camera properties. Herein, we revealed inaccuracies inherent in our current IPM approach so proper correction techniques can be applied later. We aimed to explore correction possibilities to enhance the accuracy of IPM and examine other methods that could be used as a benchmark or even a replacement, such as stereo vision and deep learning-based monocular depth estimation methods. With this work, we aimed to provide an analysis and direction for working with IPM.

Open Access: Yes

DOI: 10.3390/engproc2024079067

Towards Robust LIDAR Lane Clustering for Autonomous Vehicle Perception in ROS 2

R. C. Pozna Ernö Horváth Dániel Pup Miklos Unger

Publication Name: Proceedings 2024 IEEE International Conference on Mobility Operations Services and Technologies Most 2024

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 229-234

Description:

From LIDAR pointclouds traffic lanes, racetracks, parking lanes can be extracted with clustering algorithms. However, standard clustering algorithms like DBSCAN, K-means, and BIRCH may exhibit limited robustness in recognizing these specific geometric patterns. The current paper proposes a modification of the well-known DBSCAN algorithm which is designed for autonomous vehicle lane detection. The main idea of the proposed work is to add extra steps into the classic DBSCAN algorithm, thus regulate the cluster expansion. This modification introduces some challenges too, their subsequent resolution will be addressed in detail. To reproduce our work, both the dataset and the accompanying source code in python is shared publicly.

Open Access: Yes

DOI: 10.1109/MOST60774.2024.00031

Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions

Tamás Budai Ernö Horváth Akos Kovacs Miklos Unger Rudolf Krecht Norbert Marko

Publication Name: IEEE Network

Publication Date: 2023-07-01

Volume: 37

Issue: 4

Page Range: 282-288

Description:

Global megatrends, such as urbanization, population growth, and emerging network solutions are accelerating the development of the Connected and Autonomous Vehicles (CAVs) industry. There are many truths, some misconceptions, and even some excitement about CAVs in the public's opinion. The main objective of the current article is to provide a comprehensive review, eliminate misconceptions, and outline the future of the network optimization aspects of autonomous vehicles by presenting various multidisciplinary methods, such as cooperative perception. Given our extensive experience with CAVs, we are aiming to share some of the insights and knowledge we have gained, along with relevant use-cases and experiment results.

Open Access: Yes

DOI: 10.1109/MNET.007.2300023

Validation Process of the Computer Simulation of a Test-Purpose Self-Driving Vehicle

Á Ballagi Miklos Unger Rudolf Krecht

Publication Name: Iavvc 2023 IEEE International Automated Vehicle Validation Conference Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The aim of this article is to propose the initial validation steps for a test-purpose self-driving vehicle. The basis of the computer simulation in question is a passenger vehicle converted to be capable of specific self-driving tasks, meaning that it features a complete low-level control system, an onboard computer for high-level computation and a full sensor set consisting of laser scanners, lidars, cameras and GNSS receivers. The computer simulation of the described vehicle is created using the SVL Autonomous Vehicle Simulator and aims to completely model the behavior of the real transformed vehicle. To ensure the fidelity of the computer simulation, a set of comparative measurements are defined, which are realized using both the real vehicle and its computer simulation. The basis of comparison is, on one hand, the assessment of the vehicle control system by comparing control input and output, on the other hand, the comparison of onboard sensor measurement results.

Open Access: Yes

DOI: 10.1109/IAVVC57316.2023.10328107

Real‐time lidar‐based urban road and sidewalk detection for autonomous vehicles

R. C. Pozna Ernö Horváth Miklos Unger

Publication Name: Sensors

Publication Date: 2022-01-01

Volume: 22

Issue: 1

Page Range: Unknown

Description:

Road and sidewalk detection in urban scenarios is a challenging task because of the road imperfections and high sensor data bandwidth. Traditional free space and ground filter algorithms are not sensitive enough for small height differences. Camera‐based or sensor‐fusion solutions are widely used to classify drivable road from sidewalk or pavement. A LIDAR sensor contains all the necessary information from which the feature extraction can be done. Therefore, this paper focuses on LIDAR‐based feature extraction. For road and sidewalk detection, the current paper presents a real‐time (20 Hz+) solution. This solution can also be used for local path planning. Sidewalk edge detection is the combination of three algorithms working parallelly. To validate the result, the de facto standard benchmark dataset, KITTI, was used alongside our measurements. The data and the source code to reproduce the results are shared publicly on our GitHub repository.

Open Access: Yes

DOI: 10.3390/s22010194

Development of Point-cloud Processing Algorithm for Self-Driving Challenges

Ernö Horváth Peter Koros Miklos Unger

Publication Name: Ines 2020 IEEE 24th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2020-07-01

Volume: Unknown

Issue: Unknown

Page Range: 91-95

Description:

The paper proposes an own-developed point-cloud processing algorithm which was developed for the Autonomous Urban Concept competition organized by Shell. The approach does not intend to solve general-purpose object recognition and tracking, although the methodologies presented can be used as general solutions. Our approach will be presented in comprehensive manner, the challenges and solutions will be detailed. Also, the dysfunctional ideas will be listed, and alternative workarounds will be presented as recommendations too. As verification of the algorithm, both simulation and real-world measurements will be presented. For the sake of research and open source, we share datasets and necessary information publicly.

Open Access: Yes

DOI: 10.1109/INES49302.2020.9147201