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Traffic Sign Detection and Recognition

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 33260

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Dr. Darko Babić

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Guest Editor

Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
Interests: traffic sign recognition

Special Issue Information

Dear Colleagues,

Welcome to the special issue of Traffic Sign Detection and Recognition dedicated to traffic signs as a part of smart “infrastructure to vehicle communication“ (I2V). Traffic signs constitute a fundamental asset of the road. Pedestrians and drivers should easily notice them by day and by night in order to be warned and guided. Consequently, their shapes, colors and positions are designed to be unique and easy to distinguish by humans but development of modern vehicles with cameras and various sensors enable them to detect and recognize traffic signs.

Traffic sign detection and recognition system based on image processing have achieved significant progress, but still today it is the most important task of advanced driver assistance systems since it improves the safety and comfort of drivers. Multiple features (hardware) are available on vehicles today that detect and recognize traffic signs, variety of sensors, including radar, sonar, lidar, and camera systems, but also there are several state-of-the-art methods developed (software and algorithms), that can be used for different recognition problems.

This Special Issue invites original papers and review articles on all aspects of traffic sign recognition and detection systems, autonomous vehicles and ADAS systems. We hope the I2V community will find this special issue to be an informative and useful collection of articles.

Dr. Darko Babić
Guest Editor

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Published Papers (6 papers)

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Research

18 pages, 6836 KiB

Open AccessArticle

Traffic Sign Comprehension among Filipino Drivers and Nondrivers in Metro Manila

by Rex Aurelius C. Robielos and Chiuhsiang Joe Lin

Appl. Sci. 2022, 12(16), 8337; https://doi.org/10.3390/app12168337 - 20 Aug 2022

Cited by 4 | Viewed by 16929

Abstract

The current study examined 73 existing traffic signs in Metro Manila for their matching accuracy, matching time, and cognitive design features. A total of 60 Filipinos (30 drivers and 30 nondrivers) were voluntarily recruited to perform a matching-based comprehension test. In a matching-based comprehension test, the traffic sign is matched with the most appropriate referent name which shows a clear-cut distinction between correct and incorrect answers. To assess a sign’s acceptability in a matching test, a level of at least 67% accuracy must be obtained in a comprehension test. For the matching accuracy, 27 of the 73 traffic signs did not comply with the 67% comprehension standard set by ISO 3864-1:2011. Drivers were found to have better matching accuracy for both regulatory and warning signs compared to nondrivers. Traffic signs displayed in symbols had the lowest matching accuracy and slowest matching time. When text was added to traffic signs displayed in symbols, matching accuracy and matching time improved significantly. However, signs displayed in text only obtained the highest matching accuracy and fastest matching time. The cognitive design features, which were the measurement of a sign’s design, were also assessed through their familiarity, concreteness, complexity, and semantic distance. Cognitive design features were found to be positively correlated to matching accuracy for both regulatory and warning signs, but negatively correlated to matching time for warning signs. For signs displayed in symbols, cognitive design features were also found to be correlated to matching accuracy and matching time. To improve comprehension and road safety, semantic distance, concreteness, and familiarity are the key cognitive design features which must be considered by traffic sign designers. Also, the Department of Transportation (Philippines) could adopt the matching test of this study as a mandatory retraining requirement for the renewal of a driver’s license. In addition, our matching-based comprehension test can also be applied and extended to evaluate existing traffic signs worldwide. Full article

(This article belongs to the Special Issue Traffic Sign Detection and Recognition)

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29 pages, 1910 KiB

Open AccessArticle

Road Infrastructure Challenges Faced by Automated Driving: A Review

by Tomislav Mihalj, Hexuan Li, Dario Babić, Cornelia Lex, Mathieu Jeudy, Goran Zovak, Darko Babić and Arno Eichberger

Appl. Sci. 2022, 12(7), 3477; https://doi.org/10.3390/app12073477 - 29 Mar 2022

Cited by 10 | Viewed by 5040

Abstract

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS. Full article

(This article belongs to the Special Issue Traffic Sign Detection and Recognition)

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19 pages, 2504 KiB

Open AccessArticle

An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs

by Roxan Saleh, Hasan Fleyeh and Moudud Alam

Appl. Sci. 2022, 12(5), 2413; https://doi.org/10.3390/app12052413 - 25 Feb 2022

Cited by 8 | Viewed by 1574

Abstract

The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R² values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs. Full article

(This article belongs to the Special Issue Traffic Sign Detection and Recognition)

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19 pages, 664 KiB

Open AccessArticle

Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

by Abdolmaged Alkhulaifi, Arshad Jamal and Irfan Ahmad

Appl. Sci. 2021, 11(24), 11595; https://doi.org/10.3390/app112411595 - 7 Dec 2021

Cited by 9 | Viewed by 2102

Abstract

Traffic signs are essential for the safe and efficient movement of vehicles through the transportation network. Poor sign visibility can lead to accidents. One of the key properties used to measure the visibility of a traffic sign is retro-reflection, which indicates how much light a traffic sign reflects back to the driver. The retro-reflection of the traffic sign degrades over time until it reaches a point where the traffic sign has to be changed or repaired. Several studies have explored the idea of modeling the sign degradation level to help the authorities in effective scheduling of sign maintenance. However, previous studies utilized simpler models and proposed multiple models for different combinations of the sheeting type and color used for the traffic sign. In this study, we present a neural network based deep learning model for traffic sign retro-reflectivity prediction. Data utilized in this study was collected using a handheld retro-reflectometer GR3 from field surveys of traffic signs. Sign retro-reflective measurements (i.e., the

R_{A}

values) were taken for different sign sheeting brands, grades, colors, orientation angles, observation angles, and aging periods. Feature-based sensitivity analysis was conducted to identify variables’ relative importance in determining retro-reflectivity. Results show that the sheeting color and observation angle were the most significant variables, whereas sign orientation was the least important. Considering all the features,

R_{A}

prediction results obtained from one-hot encoding outperformed other models reported in the literature. The findings of this study demonstrate the feasibility and robustness of the proposed neural network based deep learning model in predicting the sign retro-reflectivity. Full article

(This article belongs to the Special Issue Traffic Sign Detection and Recognition)

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27 pages, 7059 KiB

Open AccessArticle

ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings

by Felipe Franco, Max Mauro Dias Santos, Rui Tadashi Yoshino, Leopoldo Rideki Yoshioka and João Francisco Justo

Appl. Sci. 2021, 11(22), 10783; https://doi.org/10.3390/app112210783 - 15 Nov 2021

Cited by 1 | Viewed by 1719

Abstract

One of the main actions of the driver is to keep the vehicle in a road lane within its markings, which could be aided with modern driver-assistance systems. Forward digital cameras in vehicles allow deploying computer vision strategies to extract the road recognition characteristics in real-time to support several features, such as lane departure warning, lane-keeping assist, and traffic recognition signals. Therefore, the road lane marking needs to be recognized through computer vision strategies providing the functionalities to decide on the vehicle’s drivability. This investigation presents a modular architecture to support algorithms and strategies for lane recognition, with three principal layers defined as pre-processing, processing, and post-processing. The lane-marking recognition is performed through statistical methods, such as buffering and RANSAC (RANdom SAmple Consensus), which selects only objects of interest to detect and recognize the lane markings. This methodology could be extended and deployed to detect and recognize any other road objects. Full article

(This article belongs to the Special Issue Traffic Sign Detection and Recognition)

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12 pages, 2542 KiB

Open AccessArticle

Traffic Light and Arrow Signal Recognition Based on a Unified Network

by Tien-Wen Yeh, Huei-Yung Lin and Chin-Chen Chang

Appl. Sci. 2021, 11(17), 8066; https://doi.org/10.3390/app11178066 - 31 Aug 2021

Cited by 13 | Viewed by 3647

Abstract

We present a traffic light detection and recognition approach for traffic lights that utilizes convolutional neural networks. We also introduce a technique for identifying arrow signal lights in multiple urban traffic environments. For detection, we use map data and two different focal length cameras for traffic light detection at various distances. For recognition, we propose a new algorithm that combines object detection and classification to recognize the light state classes of traffic lights. Furthermore, we use a unified network by sharing features to decrease computation time. The results reveal that the proposed approach enables high-performance traffic light detection and recognition. Full article

(This article belongs to the Special Issue Traffic Sign Detection and Recognition)

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