Ziyad N. Aldoski

57221045438

Publications - 8

Traffic Sign Detection and Quality Assessment Using YOLOv8 in Daytime and Nighttime Conditions

Csaba Koren Ziyad N. Aldoski

Publication Name: Sensors

Publication Date: 2025-02-01

Volume: 25

Issue: 4

Page Range: Unknown

Description:

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards.

Open Access: Yes

DOI: 10.3390/s25041027

Standardized Assessment, LiDAR-Based Measurements, and Human Perception of Traffic Signs

Csaba Koren Ziyad N. Aldoski

Publication Name: Lecture Notes in Networks and Systems

Publication Date: 2025-01-01

Volume: 1258 LNNS

Issue: Unknown

Page Range: 77-86

Description:

Traffic signs are essential informative tools for road users, with their quality regulated by international standards. Among the critical parameters for evaluation is retroreflectivity. With the rise of autonomous vehicles (AVs), new tools like LiDAR sensors and cameras have become crucial for assessing traffic signs. However, the relationship between human perception and the physical properties of these signs remains underexplored. This study compares traffic sign evaluations using retroreflectivity measurements, LiDAR data, and human assessment. Approximately 160 traffic signs were analyzed using standardized retroreflectivity measurements, with additional data collected from two different LiDAR systems mounted on an AV. Volunteers conducted human evaluations assessing visibility, legibility, and contrast with surroundings. The handheld reflectometer provided a wide range of retroreflectivity readings, while LiDAR data showed good contrast between sign faces and surroundings, though results varied between the two systems. Human assessments correlated strongly with overall appearance but showed limited correlation with the other methods. This study highlights the strengths and limitations of each evaluation approach, offering insights for improving traffic sign assessment techniques.

Open Access: Yes

DOI: 10.1007/978-3-031-81799-1_8

Impact of using colored pigments on rigid concrete pavements

Ziyad N. Aldoski Ismael H.Mosa Albarwary

Publication Name: Aip Conference Proceedings

Publication Date: 2024-06-18

Volume: 2944

Issue: 1

Page Range: Unknown

Description:

Concrete is one of the leading construction materials known for its strength and durability. However, it is an aesthetically unfriendly and somewhat boring material due to its dull gray color that should be covered or painted. Recently, there has been a growing use of colored concrete materials by adding colored pigments to the mixture. In the Kurdistan Region of Iraq (KRI), there is a need to incorporate full-depth colored concrete into streets and roadways projects, especially for road ramps, cycle lanes, pedestrian crosswalks, sidewalks, and car parking spaces for disabled people, etc. This research aims to manufacture colored concrete pavement that might provide more durable, longer-lasting colored concrete features by adding a suitable pigment amount. This study examined concrete's compressive and tensile strength for M2O grade concrete colored with (0, 3, 5, 7, and 9) percent of red iron oxide pigment. The dosage of added coloring is referenced to the weight of the binder (cement). Test results show that adding color pigment to the concrete does not significantly affect its mechanical properties, and the colored concrete can be used for structural purposes. It is concluded that the optimal dosages of color pigment for the concrete are 5% in the fresh state and 7% in the hardened condition. Finally, using colored concrete in road facilities planning will be a safe, convenient, and economical way to control traffic conflicts.

Open Access: Yes

DOI: 10.1063/5.0204575

Improving Autonomous Vehicle Perception through Evaluating LiDAR Capabilities and Handheld Retroreflectivity Assessments

Csaba Koren Ziyad N. Aldoski

Publication Name: Sensors

Publication Date: 2024-06-01

Volume: 24

Issue: 11

Page Range: Unknown

Description:

Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a need for ongoing advancements in traffic sign evaluation methodologies. This paper comprehensively analyzes the relationship between traffic sign retroreflectivity and LiDAR intensity to enhance visibility and communication on road networks. Using Python 3.10 programming and statistical techniques, we thoroughly analyzed handheld retroreflectivity coefficients alongside LiDAR intensity data from two LiDAR configurations: 2LRLiDAR and 1CLiDAR systems. The study focused specifically on RA1 and RA2 traffic sign classes, exploring correlations between retroreflectivity and intensity and identifying factors that may impact their performance. Our findings reveal variations in retroreflectivity compliance rates among different sign categories and color compositions, emphasizing the necessity for targeted interventions in sign design and production processes. Additionally, we observed distinct patterns in LiDAR intensity distributions, indicating the potential of LiDAR technology for assessing sign visibility. However, the limited correlations between retroreflectivity and LiDAR intensity underscore the need for further investigation and standardization efforts. This study provides valuable insights into optimizing traffic sign effectiveness, ultimately contributing to improved road safety conditions.

Open Access: Yes

DOI: 10.3390/s24113304

ASSESSMENT OF TRAFFIC SIGN RETROREFLECTIVITY FOR AUTONOMOUS VEHICLES: A COMPARISON BETWEEN HANDHELD RETROREFLECTOMETER AND LIDAR DATA

Csaba Koren Ziyad N. Aldoski

Publication Name: Archives of Transport

Publication Date: 2024-01-01

Volume: 70

Issue: 2

Page Range: 7-26

Description:

This study investigates the critical role of retroreflectivity in traffic signs, particularly in the context of autonomous vehicles (AVs), where accurate detection is paramount for road safety. Retroreflectivity, influencing visibility and legibility, is essential for ensuring safe road conditions. The study aims to assess traffic sign retroreflectivity using handheld retroreflectometers and LiDAR data, offering a comprehensive comparison of results with a specific focus on the RA1 and RA2 traffic sign classes. In a real-world setting, an AV equipped with LiDAR sensors, GPS units, and a stereo camera collects data on traffic signs, including point cloud attributes such as intensity and density. Simultaneously, a handheld retroreflectometer measures retroreflectivity coefficients from identified traffic signs. While retroreflectometers provide precision, they face limitations regarding time-consuming measurements and handling large or elevated signs. In contrast, LiDAR systems efficiently evaluate retroreflective features for numerous signs without such constraints. Despite both methods consistently yielding accurate retroreflectivity, the study reveals a limited correlation between LiDAR point cloud data and handheld retroreflectivity coefficients. The implications of these findings are significant, particularly in the selection and maintenance of retroreflective materials in traffic signs, with direct repercussions on overall road safety. The results offer valuable insights into leveraging LiDAR technology to enhance AVs' detection capabilities. Recommendations for further research include exploring factors influencing LiDAR intensity, establishing a more accurate relationship between intensity and retroreflectivity, correcting the point cloud during intensity calibration, and testing empirical prediction models with a larger sample size. These endeavors aim to generate a robust regression graph and determine correlation coefficients, providing a more nuanced understanding of the intricate relationship between LiDAR data and handheld retroreflectivity coefficients in the context of traffic sign assessment.

Open Access: Yes

DOI: 10.61089/aot2024.qxy24g93

Traffic and Safety Characteristics for the Kurdistan Area: Duhok City

Hamid Athab Al-Jameel Fatimah Fahem Al-Khafaji Ziyad N. Aldoski

Publication Name: Aip Conference Proceedings

Publication Date: 2023-09-01

Volume: 2806

Issue: 1

Page Range: Unknown

Description:

This study focuses mainly on investigating some traffic characteristics according to field observations. The road between Girsheen and Suhela Intersections, in Duhok city, has been examined for some traffic characteristics such as flow, traffic composition, and traffic accidents. Viedo cameras have been adopted to determine the level of service besides using the test car method for identifying the average travel time and speed for the road. Then, the LOS for both directions is relatively low due to insufficient capacity for the traffic volume, which might affect the pavement's service life. On the other hand, the total number of accidents recorded for six months is 73, and fatalities were recorded from 13/3/2020 to 24/9/2020. The percentage of accidents resulting in injuries is 5.48%, and the injury rate is 46 per million vehicle km. The improvement of the injury rate is by 36.98%. This study forecasted an improvement of injury rate by 24.65%.

Open Access: Yes

DOI: 10.1063/5.0162666

Road Safety and Sustainability: A Comparison of Country Rankings

Csaba Koren Ziyad N. Aldoski

Publication Name: Chemical Engineering Transactions

Publication Date: 2023-01-01

Volume: 107

Issue: Unknown

Page Range: 277-282

Description:

Road traffic deaths are a crucial problem in the world. At the same time, sustainability is a global issue. Numerous publications are dealing with road safety and sustainability separately as a basis for country rankings, but if you are searching for “road safety and sustainability”, no relevant results are found. The research question of this paper is: How is the safety performance of countries with good sustainability ranks? Vice versa: are countries with lower sustainability ranks more dangerous in traffic? To answer these questions, international sustainability and safety ranking scales were compared using Spearman correlation coefficients. The key finding of the paper is that there is a strong correlation between sustainability and the safety ranking of countries. However, there are some exceptions, countries with significantly different sustainability and safety performances.

Open Access: Yes

DOI: 10.3303/CET23107047

Impact of Traffic Sign Diversity on Autonomous Vehicles A Literature Review

Csaba Koren Ziyad N. Aldoski

Publication Name: Periodica Polytechnica Transportation Engineering

Publication Date: 2023-01-01

Volume: 51

Issue: 4

Page Range: 338-350

Description:

Traffic sign classification is indispensable for road traffic systems, including automated ones. There is a fundamental difference in the visual appearance of traffic signs from one country to another. Each dataset has its design standards and regulations based on shape, color, and information content, making implementing classification and recognition techniques more difficult. This paper aims to assess the influence of traffic sign diversity on autonomous vehicles (AVs) by reviewing several previous studies, comparing, summarizing their results, and focusing on classifying and detecting traffic sign datasets based on color, shape, and deep learning spaces using various methods and applications. Furthermore, it covers the main challenges facing road designers and planners considering changes to road safety infrastructure. It will be argued that compiling and standardizing a comprehensive global database of traffic signs is very difficult because it is costly and complex in application. However, it is still one of the possible solutions for the coming decades. Recommendations for future developments are also presented in this study.

Open Access: Yes

DOI: 10.3311/PPtr.21484