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Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over the coming years, the advancement of driverless transport systems for people and goods that are designed to be used on fixed routes will revolutionize the transportation system. Therefore, for a safe transportation system, detecting and recognizing traffic signals based on computer vision has become increasingly important. Deep learning approaches, particularly convolutional neural networks, have shown exceptional performance in various computer vision applications. The goal of this research is to precisely detect and recognize traffic signs that are present on the streets using computer vision and deep learning techniques. Previous work has focused on symbol-based traffic signals, where popular single-task learning models have been trained and tested. Therefore, several comparisons have been conducted to select accurate single-task learning models. For further improvement, these models are employed in a multi-task learning approach. Indeed, multi-task learning algorithms are built by sharing the convolutional layer parameters between the different tasks. Hence, for the multi-task learning approach, different experiments have been carried out using pre-trained architectures like, for instance, InceptionResNetV2 and DenseNet201. A range of traffic signs and traffic lights are employed to validate the designed model. An accuracy of 99.07% is achieved when the entire network has been trained. To further enhance the accuracy of the model for traffic signs obtained from the street, a region of interest module is added to the multi-task learning module to accurately extract the traffic signs available in the image. To check the effectiveness of the adopted methodology, the designed model has been successfully tested in real-time on a few Riyadh highways. [ABSTRACT FROM AUTHOR]

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems. [ABSTRACT FROM AUTHOR]

Intelligent Transportation System (ITS) aims to strengthen the connection between vehicles, roads, and people. As the important road information in ITS, intelligent detection of traffic signs has become an important part in the intelligent vehicle. In this paper, a lightweight vehicle mounted multi-scale traffic sign detector is proposed. First, guided by the attention fusion algorithm, an improved feature pyramid network is proposed, named AFPN. Assign weights according to the importance of information and fuse multi-dimensional attention maps to improve feature extraction and information retention capabilities. Second, a multi-head detection structure is designed to improve the positioning and detection capability of the detector. According to the target scale, the corresponding detection head is constructed to improve the target detection accuracy. The experimental results show that compared with other state-of-the-art methods, the proposed method not only has excellent detection accuracy with 50.3% for small targets and 64.8% for large targets but also can better trade-off detection speed and detection accuracy. Furthermore, the proposed detector is deployed on the Jetson Xavier NX and integrated with the vehicle-mounted camera, inverter, and LCD to realize real-time traffic sign detection on the vehicle terminal, and the speed reaches 25.6 FPS. [ABSTRACT FROM AUTHOR]

Traffic signs are the globally essential map features for the era of autonomous driving and smart cities. Traffic sign recognition is a difficult task due to their multiple shapes, sizes, color, occlusions and complicated driving scenes. For automatic traffic signs and classification, a robust and efficient system is needed with a highly accurate prediction rate. Therefore, a novel goat-based convolutional neural–Kalman framework (GbCN-KF) is proposed to detect the traffic signs for smart cities. Primarily, the input traffic sign images are noise-filtered by the Kalman function and the relative features for the recognition process were extracted. Further, traffic signs are recognized by matching the features with the trained set and classified using the goat fitness function. The system is tested with the BTSC and GTSRB database. The performance score was evaluated for the datasets and compared with the prevailing recognition models. The model recorded a high accuracy percentage of 99.89% and 99.94% for the tested BTSC and GTSRB datasets. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The importance of how cognitive processes (Reasoning) influence on the understanding and the mental representation of road-side elements in form of the movement effects on the path driving must be studied in-depth. Literature shows that the key point to explain the influence of attention on movement is the meaning of the object being processed; literature also shows that the Reasoning with those cues we pay attention to has an effect on driving. By using a driving-simulation task, traffic signs have been tested on their effect on movement. Results show that the least-representative-of-their-meaning signs deviate participants´ movement path more intensively than the most-representative-of-their-meaning signs. Conclusions: Traffic accidents are well-known for their. The results here reported help to improve the cognitive Ergonomics of every road element, specially, traffic signs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deontic norms are expected to impose individuals' control over their behavior. In this paper, we address such norms presented in traffic signs and test their influence over executive control functions. For Experiment 1, we develop a traffic flanker task in which the typical neutral arrows are replaced with traffic prohibition/obligation signs. Experiment 2 isolated the deontic aspect of the signs using simple arrows on red, blue, and green backgrounds and either primed them to be interpreted as traffic signs or as elements of a gaming console controller. Results in both studies show evidence of controlling context interferences more efficiently when dealing with deontic (traffic) signs than with simple arrows (Experiment 1) or with similar perceptive targets when primed with a deontic context than with a gaming context (Experiment 2). In both studies, obligation/blue signs mitigate flanker effects less than prohibition/red signs. Stimuli color affects the alertness of the cognitive system, with the color red being, by itself, a cue for increased control. Based on temporal analysis, we further discuss these results as evidence of an increase in proactive control that aims to prevent the occurrence of undesirable influence. [ABSTRACT FROM AUTHOR]

The current computational advance allows the development of technological solutions using tools, such as mobile robots and programmable electronic systems. We present a design that integrates the Khepera IV mobile robot with an NVIDIA Jetson Xavier NX board. This system executes an algorithm for navigation control based on computer vision and the use of a model for object detection. Among the functionalities that this integration adds to the Khepera IV in generating guided driving are trajectory tracking for safe navigation and the detection of traffic signs for decision-making. We built a robotic platform to test the system in real time. We also compared it with a digital model of the Khepera IV in the CoppeliaSim simulator. The navigation control results show significant improvements over previous works. This is evident in both the maximum navigation speed and the hit rate of the traffic sign detection system. We also analyzed the navigation control, which achieved an average success rate of 93 % . The architecture allows testing new control techniques or algorithms based on Python, facilitating future improvements. [ABSTRACT FROM AUTHOR]

This article proposes an algorithm for recognizing road signs based on a determination of their color and shape. It first searches for the edge segment of the road sign. The boundary curve of the road sign is defined by the boundary of the edge segment. Approximating the boundaries of a road sign reveals its shape. The hierarchical road sign recognition system forms classes in the form of a sign. Six classes are at the first level. Two classes contain only one road sign. Signs are classified by the color of the edge segment at the second level of the hierarchy. The image inside the edge segment is cut at the third level of the hierarchy. The sign is then identified based on a comparison of the pattern. A computer experiment was carried out on two collections of road signs. The proposed algorithm has a high operating speed and a low percentage of errors. [ABSTRACT FROM AUTHOR]

Traffic signs are updated quickly, and there image acquisition and labeling work requires a lot of manpower and material resources, so it is difficult to provide a large number of training samples for high-precision recognition. Aiming at this problem, a traffic sign recognition method based on FSOD (few-shot object learning) is proposed. This method adjusts the backbone network of the original model and introduces dropout, which improves the detection accuracy and reduces the risk of overfitting. Secondly, an RPN (region proposal network) with improved attention mechanism is proposed to generate more accurate target candidate boxes by selectively enhancing some features. Finally, the FPN (feature pyramid network) is introduced for multi-scale feature extraction, and the feature map with higher semantic information but lower resolution is merged with the feature map with higher resolution but weaker semantic information, which further improves the detection accuracy. Compared with the baseline model, the improved algorithm improves the 5-way 3-shot and 5-way 5-shot tasks by 4.27% and 1.64%, respectively. We apply the model structure to the PASCAL VOC dataset. The results show that this method is superior to some current few-shot object detection algorithms. [ABSTRACT FROM AUTHOR]

Traffic sign retroreflectivity is critical for nighttime visibility, an important factor in driver safety. Current methods of sign retroreflectivity assessment are expensive, time-consuming, dangerous, or subjective. There is an urgent need to explore an alternative method that is cost-effective, safe, objective, and can be operated during daytime or nighttime. One such method is mobile lidar. However, a methodology utilizing lidar cloud data for practical retroreflectivity condition assessment is still lacking because of the inability to numerically correlate lidar retro-intensity readings to the retroreflectivity standard set by the Manual on Uniform Traffic Control Devices (MUTCD). In addition, there is also a need to explore sign deterioration behavior using real-world lidar data. In this study, we (1) propose a practical, categorical traffic sign condition assessment using lidar data; (2) establish a preliminary correlation between the retro-intensity and retroreflectivity readings to determine the minimum retro-intensity thresholds for condition assessment of different sheeting types and colors; (3) validate the accuracy of the assessment by comparing it with standard nighttime visual inspection outcomes; (4) demonstrate the practical implementation through a feasibility study at Georgia Interstate 285; and (5) reveal the retro-intensity deterioration trends using historical lidar cloud data. The results show that the proposed methodology can reliably yield results comparable to manual measurements, potentially reducing sign retroreflectivity condition assessment effort, increasing the transportation agency’s productivity, and filling gaps where manual assessment is not possible. Additionally, the retro-intensity deterioration trends can help transportation agencies to understand the long-term behavior of sign retro-intensity and predict the optimal timing for sign replacement. [ABSTRACT FROM AUTHOR]

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars. [ABSTRACT FROM AUTHOR]

Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the previously proposed quanvolutional neural network (QuanvNN) using a randomly generated quantum circuit improves the image classification accuracy of a fully connected neural network against the Modified National Institute of Standards and Technology (MNIST) dataset and the Canadian Institute for Advanced Research 10 class (CIFAR-10) dataset from 92.0% to 93.0% and from 30.5% to 34.9%, respectively. We then propose a new model referred to as a Neural Network with Quantum Entanglement (NNQE) using a strongly entangled quantum circuit combined with Hadamard gates. The new model further improves the image classification accuracy of MNIST and CIFAR-10 to 93.8% and 36.0%, respectively. Unlike other QML methods, the proposed method does not require optimization of the parameters inside the quantum circuits; hence, it requires only limited use of the quantum circuit. Given the small number of qubits and relatively shallow depth of the proposed quantum circuit, the proposed method is well suited for implementation in noisy intermediate-scale quantum computers. While promising results were obtained by the proposed method when applied to the MNIST and CIFAR-10 datasets, a test against a more complicated German Traffic Sign Recognition Benchmark (GTSRB) dataset degraded the image classification accuracy from 82.2% to 73.4%. The exact causes of the performance improvement and degradation are currently an open question, prompting further research on the understanding and design of suitable quantum circuits for image classification neural networks for colored and complex data. [ABSTRACT FROM AUTHOR]

Summary: The detection and classification of traffic signs is a major challenge for self‐driving vehicles. The task can be narrowed down to detecting and classifying Indian Cautionary Traffic Signs (ICTS). In this proposed work, the difficulty of detecting and identifying Indian Cautionary Traffic Signs is addressed, and sincere attempts have been made to attain a possible solution using a Generative Adversarial Network (GAN), Improved Mask R‐CNN, and GrabCut algorithms. Data augmentation is done using Cascade Pyramid Generative Adversarial Network (CP‐GAN) to upsize the data set. The Mask R‐CNN with certain adoptions termed Improved mask RCNN is used in conjunction with the Grab‐Cut method to handle ICTS detection and identification through automatic end‐to‐end learning. Initially, Improved Mask R‐CNN generates a pixel‐by‐pixel segmentation mask for each item in the input sample. Masks developed using Improved Mask R‐CNN are not always clean, that is, some background pixels are often seen in the foreground segmentation. Hence, the generated masks are refined using the Grab Cut algorithm to enhance image segmentation. This combined approach works well in isolating the traffic signs from the ground truth images. Improved Mask R‐CNN attained better performance in the overall performance of traffic sign detection in the Indian data‐set. This method recognizes 40 different types of cautionary traffic signs from the unique Indian data set. The results are provided for complicated traffic sign categories that have not been addressed before. The proposed technique is trained and evaluated on ICTS, GTSDB, STSD, LISA, and DITS data sets, and it is also compared against cutting‐edge object recognition methods like Mask RCNN, Faster RCNN, SSD, and YOLOv3. [ABSTRACT FROM AUTHOR]

The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter&histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic Signal Recognition Benchmark- (GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)- General Regression Neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system. [ABSTRACT FROM AUTHOR]

Aiming at the decrease in the accuracy of traffic sign recognition due to dim light in the night environment, this paper proposes an improved you only look once version 5 (YOLOv5) algorithm to reduce carbon emissions. An improved adaptive histogram equalization method is designed to adjust the brightness and contrast of the image and highlight the detail information of traffic signs. In response to the higher requirements of the driving assistance system on the recognition model processing speed, the model is lightened and the standard convolution method of the backbone network is designed as a depth-separable convolution method, which greatly reduces the number of model parameters. To address the problem of feature loss during model learning, an improved feature pyramid AAM SPPF path aggregation network (AS-PAN) structure is proposed to enhance the learning capability of the model by adding an adaptive attention module to the Neck head and a spatial pyramid pooling module before its P3 and P4 outputs. Finally, the traditional non-maximum suppression (NMS) generates prediction frames by replacing the traditional NMS with weighted frame fusion weighted boxes fusion (WBF), which changes all possibility target frames from discard to fusion. Experiments demonstrate that the improved algorithm achieves improved detection accuracy, decreased processing time for a single image and low carbon emissions in the traffic sign recognition process compared with the original YOLOv5 algorithm in a self-built nighttime environment dataset. [ABSTRACT FROM AUTHOR]

This research demonstrates the percentage of drivers and pedestrians understanding the traffic sign in Dhaka, Bangladesh. The survey was conducted among 634 drivers inside and outside of Dhaka city. Moreover, 508 pedestrians participated in the survey within Dhaka city. In comparison, there were 863 male respondents and 279 female respondents among 1142 respondents. The survey took the form of multiple-choice questions that included the picture attached to each sign. However, the survey questionnaires included a few questions regarding driver gender, age, educational qualification, and driving experience. Similarly, the survey questionnaires for pedestrians had also been discussed questions regarding pedestrian gender, age, educational qualification, and job status. The overall traffic sign understanding of drivers was 68.68%. Moreover, the comprehensive traffic signs understanding of pedestrians was 64.5%. The findings showed that the drivers had a medium degree of understanding of the traffic sign’s meaning. However, the study results showed that efforts are needed to educate the drivers and pedestrians about the proper interpretation and reaction to traffic signals. [ABSTRACT FROM AUTHOR]

Road maintenance is a key aspect of road safety and resilience. Traffic signs are an important asset of the road network, providing information that enhances safety and driver awareness. This paper presents a method for the recognition and geolocation of vertical traffic signs based on artificial intelligence and the use of a low-cost mobile mapping system. The approach developed includes three steps: First, traffic signals are detected and recognized from imagery using a deep learning architecture with YOLOV3 and ResNet-152. Next, LiDAR point clouds are used to provide metric capabilities and cartographic coordinates. Finally, a WebGIS viewer was developed based on Potree architecture to visualize the results. The experimental results were validated on a regional road in Avila (Spain) demonstrating that the proposed method obtains promising, accurate and reliable results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Traffic & Transportation Research / Trafik ve Ulaşım Araştırmaları Dergisi is the property of Journal of Traffic & Transportation Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Against the background of studies on redundancy in law that completely omit the visual element in law and of studies on traffic signs that are laconic about their redundancies, the present study proposes more focused investigation into the redundancies of traffic signs. After presentation of the broader context of existing studies on traffic signs and on redundancy in law, and following a discussion of the direct inspiration for embarking upon research into this topic, the article moves to present and discuss six proposed types of redundancies of signs. Utilizing Franciszek Studnicki's distinction between sign-types and sign-realizations, and given that traffic signs exist in various complicated relationships with each other, with written formulations in legal texts, and with the environments in which they are placed, the study comments on six types of redundancy: (1) sign-type–basic task(s) of road traffic law; (2) of sign-type's elements; (3) sign-type–sign-type; (4) sign-type–legal text; (5) sign-realization–sign-realization; and (6) sign-realization–environment. Analysis of each type is supported through examples, various subdivisions, and additional lines of inquiry. The study has value for both strictly theoretical and more practical enterprises related both to traffic signs themselves and to the wider realm of visualization of norms. [ABSTRACT FROM AUTHOR]

The location of highway signs has a significant impact on timely route-finding and also on avoiding potential driver confusion. This paper proposes an approach based on three-dimensional spatial analysis to determine the best attributes for signs and traffic flow concerning the visibility of signs. The position, height and perceived area of signs; maximum speed limit; and size of cars were utilized as indicators. Traffic flow was simulated using the Nagel-Schreckenberg model, while other vehicles were considered as dynamic obstacles. The performance of the proposed approach was evaluated in a case study by designing different scenarios. The results reveal that the proposed method can be used as an appropriate tool for evaluating the visibility of existing signs along highways and for determining the optimal place and height for new signs in three-dimensional, based on several parameters such as the maximum allowable speed, traffic flow, average density, and type of vehicles. [ABSTRACT FROM AUTHOR]

When approaching a signalized grade crossing, drivers may run the red light due to the absence of yellow signal or neglect of the warning. Some warning countermeasures such as traffic signs and in-vehicle audio warning (IVAW) have the potential to improve drivers' risk perception abilities and safety at grade crossings. In this study, a driving simulator-based experiment was conducted to investigate the effects of proposed signs and markings (PSM) and IVAW on driving behaviors at grade crossings with flashing light (FLGCs). A conceptual model was developed which divided the stop/go process into three successive stages, i.e., approach stage, reaction stage and action stage. Our experiment results showed the proposed warning countermeasure could significantly improve drivers' performance of each stage by lowering driving speed and promoting drivers to take early and smooth brake actions. The findings suggest that the designed countermeasures have the potential to reduce FLGCs crashes and provide safety benefits in foggy weather. Moreover, human factors such as gender should be considered while deploying the collision-avoidance warning countermeasures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Road traffic signs are part of the road infrastructure components meant to ensure the safe use of roads by motorists and pedestrians alike. A good knowledge and comprehension of road signs helps ensure smooth flow of traffic, improves safety for other motorists and provides drivers with good reaction time to comply with the message dictated by the signs. Abu Dhabi, being the capital of the UAE, brings together people from all over the world for work, business or tourism. Abu Dhabi has quality roads and traffic signage together with an efficient police force that enforces adherence to traffic rules. Despite all these and the reduced fatalities in absolute terms, traffic violations have been increasing exponentially, resulting in five million traffic fines in 2019 for a population of about 2.9 million inhabitants. This study sought to assess motorists' comprehension of the various traffic signs used across the UAE. The results of the survey (N = 200) revealed that the drivers were able to correctly identify 77% of the road signs in the survey. It was also found that the respondents failed to correctly identify traffic signs within the specific category referred to as "advance warning signs". Respondents' education, nationality, gender, and marital status did not have any statistically significant effects on the results compared to earlier studies. It is recommended that more attention be focused on driver education and training. [ABSTRACT FROM AUTHOR]

Copyright of Forest Engineering is the property of Forest Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effect of traffic signs in the driving behavior is not fully understood yet. The knowledge of humans' cognitive processing of the meaning of traffic signs will improve response time to those signs and the decision making carried out by drivers. Literature shows that signs – that are not-well designed – produce counterproductive effects on movement. This study sounds out which are precisely the Ecuadorian, informative traffic signs more cognitive ergonomic for participants and classify them by using the criteria of representativity, univocity and numbers of errors provoked by them. Doing so, it can be detected which traffic signs need to be redesigned. The economic, social and psychological consequences of car accidents are enough important to carry out this study, which aims to increase road safety. [ABSTRACT FROM AUTHOR]

Since the middle of the last century, normative language has been much studied. In particular, the normative function performed by certain sentences and by certain speech acts has been investigated in depth. Still, the normative function performed by certain physical artifacts designed and built to regulate human behaviors has not yet been thoroughly investigated. We propose to call this specific type of artifacts with normative intent 'deontic artifacts'. This article aims to investigate this normative phenomenon that is so widespread in our daily reality, but so often forgotten by scholars of norms and normativity. [ABSTRACT FROM AUTHOR]

Copyright of Roads & Bridges / Drogi i Mosty is the property of Road & Bridge Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In highway traffic, different traffic signs have different influences on the driver's gaze behavior, affecting the safety of drivers and highway driving. To solve this matter, the research based on visual characteristic analysis aims to scientifically evaluate such influences, thereby indirectly reducing the occurrence of highway traffic accidents. Via designing tests to simulate the influence of highway traffic signs on curve highway sections on driver's visual system, our study adopted the eye tracking system as the visual characteristic acquisition device in the test; then this paper used gaze duration, gaze frequency, and average glance speed as the evaluation indicators of gaze behavior to compare the influence of different traffic signs on the gaze behavior and analyze the variation laws. Moreover, to further evaluate the influence of traffic signs on the safe driving behavior of drivers, based on the test results, this paper also gave a qualitative evaluation on the driver's visual load in highway road sections with different traffic signs. The test results in this paper provide scientific suggestions for the layout of highway traffic signs so as to effectively reduce the occurrence of highway traffic accidents, and this study is of important practical significance. [ABSTRACT FROM AUTHOR]

Copyright of Electronics & Control Systems is the property of National Aviation University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic violations in Indonesia were mostly committed by young drivers. Surprisingly, university students ranked third as traffic violators despite they are expected to have better understanding of traffic signs instead of others. This study was conducted to investigate the influence factors that might encourage university students as well-educated young drivers to commit traffic violations through several situation scenarios. Five prohibitory traffic signs (no u-turn, no parking, stop sign, maximum speed limit, no stopping) were subjectively evaluated by 212 university students (18–23 years old, having valid car and or motorcycle driving license) whether they will violate these signs in 7 situation scenarios as indicators based on human-related factors (no police officers, in a hurry, imitate mistakes, sleep deprivation and fatigue) and environmental-related factors (quiet roads, night driving, driving in heavy rain). From the results, the influence varied for each sign. Both factors influenced no u-turn, no parking and no stopping signs. Human-related factors influenced maximum speed limit sign while environmental-related factors influenced stop sign. This study has provided a useful initial insight to understand young driver's behavior to violate these prohibitory traffic signs. For further studies, it can be applied to other signs with different respondent profiles. Apart from that, the relationship between indicators can also be investigated. [ABSTRACT FROM AUTHOR]

Adults with dyslexia may find difficulties in reading the messages on variable message signs (VMS) while driving. These signs are an essential part of the traffic communication systems, aimed at informing road users of special circumstances, such as congestion, traffic diversion, or unexpected events. A driving simulation experiment was conducted to test if complementary audio versions of the VMS would be helpful for drivers with dyslexia. Twenty adults with dyslexia and 20 matched controls participated. They were asked to classify the messages displayed on VMS posted along a simulated route, which was completed twice: one with text VMS (visual condition), and another one with text VMS plus complementary audio messages (audio + visual condition). The results showed that in the ordinary, visual condition, the participants with dyslexia needed to be closer to the VMS than controls to correctly classify the message, but, crucially, these group differences vanished when the driver received an audio version of the message (audio + visual condition). Moreover, the availability of audio versions had positive effects for all participants, as shown by higher accuracy in the message classification task, as well as better driving performance. Therefore, technologies aimed at providing audio versions of VMS can help drivers with dyslexia. A mobile application, READit VMS, which is able to provide real-time complementary audio versions of VMS, is presented as an example. [ABSTRACT FROM AUTHOR]

In the past decade, artificial intelligence and Internet of things (IoT) technology have been rapid development, gradually began to integrate with each other, especially in coming 5G era. Admittedly, image recognition is the key technology due to a huge number of video cameras integrated in intelligent IoT equipment, such as driverless cars. However, the rapidly growing body of research in adversarial machine learning has demonstrated that the deep learning architectures are vulnerable to adversarial examples. Thus, the raises questions about the security of intelligent Internet of thing (IoT) and trust sensitive areas. This emphasizes the urgent need for practical defense technology that can be deployed to real-time combat attacks at any time. Well-crafted small perturbations lead to the misclassification of legitimate images by neural networks, but not the human visual system. It is worth noting that many attack strategies are designed to disrupt image pixels in a visually imperceptible manner. Therefore, we propose a new defense method and take full advantage of 5G high-speed bandwidth and mobile edge computing (MEC) effectively. We use singular value decomposition (SVD) which is the optimal approximation of matrix in the sense of square loss to eliminate the perturbation. We have conducted extensive and large-scale experiments with German Traffic Sign Recognition Benchmark (GTSRB) datasets and the results show that adversarial attacks, such as Carlini-Wagner's l2, Deepfool, and I-FSGM, can be better eliminated by the method and provide lower latency. [ABSTRACT FROM AUTHOR]

Traffic-sign recognition plays an important role in road transportation systems. This letter presents a novel two-stage method for detecting and recognizing traffic signs from mobile Light Detection and Ranging (LiDAR) point clouds and digital images. First, traffic signs are detected from mobile LiDAR point cloud data according to their geometrical and spectral properties, which have been fully studied in our previous work. Afterward, the traffic-sign patches are obtained by projecting the detected points onto the registered digital images. To improve the performance of traffic-sign recognition, we apply a convolutional capsule network to the traffic-sign patches to classify them into different types. We have evaluated the proposed framework on data sets acquired by a RIEGL VMX-450 system. Quantitative evaluations show that a recognition rate of 0.957 is achieved. Comparative studies with the convolutional neural network (CNN) and our previous supervised Gaussian–Bernoulli deep Boltzmann machine (GB-DBM) classifier also confirm that the proposed method performs effectively and robustly in recognizing traffic signs of various types and conditions. [ABSTRACT FROM AUTHOR]

Because of the hierarchical significance of traffic sign images, the traditional methods do not effectively control and extract the brightness and features of layered images. Therefore, an automatic recognition algorithm for traffic signs based on a convolution neural network is proposed in this paper. First, the histogram equalization method is used to pre-process the traffic sign images, with details of the images being enhanced and contrast of the images improved. Then, the traffic sign images are recognized by a convolution neural network and the large scale structure of information in the traffic sign images are obtained by using a hierarchical significance detection method based on graphical models. Next, the area of interest in the traffic sign images are extracted by using the hierarchical significance model. Finally, the Softmax classifier is selected to classify the input feature images to realize the automatic recognition of traffic signs. Experimental results show that the proposed algorithm can control the brightness of traffic sign images, which can accurately extract image regions of interest and complete the automatic recognition of traffic signs. [ABSTRACT FROM AUTHOR]

Growth in the transportation sector in urban areas was very high. This resulted in an increase in the number of vehicles and congestion on the highway. Public Transportation was a vehicle that causes a lot of traffic jam and made the road feel more crowded. In addition, public transportation also often violated rules such as entering into a residential housing lane to avoid overcrowding of vehicles on the highway. The violation of public transportation was by entering and passing through the residential area which was an area that must be free from the flow of public transportation. In addition, there were also traffic signs to prohibit public transportation from passing. To overcome this problem, this study proposed a system to identify and distinguish types of public transportation from other types of vehicles based on colour images. The system processes were carried out through several stages, including offline video capture, video frame processes, background and foreground separation (background subtraction), morphology (opening), bitwise and, images rectangular crop, HSV colour space conversion and HSV histogram creation. The results of the HSV colour space conversion in the histogram were used for the identification process in determining whether the city transportation existed or not by using Learning Vector Quantization method. The results of the system testing using Learning Vector Quantization method with 30Fps frame rate video test data were capable to recognize 66 images of public transportation and not public transportation from 78 videos of car typed of vehicle, obtained success with a percentage of 84.62%. And 60Fps frame rate video test data was able to recognize 71 images of public transportation and not public transportation from 78 videos of car typed of vehicle, obtained success with a percentage of 91.03%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A widespread opinion holds that norms and codes of conduct as such can only be established via words, that is, in some lexical form. This perspective can be criticized: some norms produced by human acts are not word-based at all. For example, many norms are actually conveyed through graphics (e. g. road signs and land-use maps), sounds (e. g. the referee's whistle), a silent gesture (the traffic warden's signal to halt). In this article, we will focus on the norms that are created by means of drawings and can be termed "drawn norms" or "graphical norms." Specifically, we will inquire into the phenomenon of graphical norms with particular regard to traffic signs and land-use plans, and we will discuss the philosophical and legal problems to which these phenomena give rise. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The most of legal theories in the twentieth century have always asserted that rules are product of linguistic utterances and that they have nothing to do with "visual culture". In this paper I show, on the contrary, that the visual dimension is crucial to understand and found some legal-philosophical discourse. The relationship between images and law is always bi-directional, by the first direction following the way from law to images, and by the second one, vice versa, passing from images into the universe of normative discourse. In these pages I do not explore the second direction; I limit myself to investigate the first way asking two questions relevant for the construction of the legal order: Are there visual signs in the normative language? And, if so, what function do they have? [ABSTRACT FROM AUTHOR]

Copyright of Journal of Qadisiyah Computer Science & Mathematics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When driving a vehicle, do we read the words displayed on traffic signs just as we do in more standard conditions? In the driving context, stimulus quality is generally worse, and reading has to be performed at the same time as we are doing other tasks. In the present work, we examined the effects of word frequency and word length on reading in such circumstances. A stimulus presentation mimicking the approach to the traffic sign increased the effect of word frequency, but not the effect of word length, on reading latency. In addition, performing the reading task while driving along a simulated route produced similar results. Therefore, in the context of the driving activity, the advantage of high-frequency over low-frequency words seems to be even more relevant than in standard conditions. Traffic sign designers can consider these results when deciding the most appropriate word in a variable message sign. [ABSTRACT FROM AUTHOR]

Traffic sign detection is a crucial step for automatic driving and Intelligent Transportation. Promising results have been achieved in the area of traffic sign detection, but most of them are limited to ideal environment, where the traffic signs are very clear and large. Actually, traffic sign detection is always realized based on object detection methods. However, existing object detection methods failed to detect most of the traffic signs, especially in surveillance videos or driving recorder videos. In fact, traffic signs, i.e. traffic lights, or distant road signs in driving recorded video, always cover less than 5% of the whole image in the view of camera. Therefore, in this paper, we dedicate an effort to propose a real-time small traffic sign detection approach based on revised Faster-RCNN. More specifically, firstly, we use a small region proposal generator to extract the characteristics of small traffic signs. That is to say, considering that the stride of generator is too large, we remove the pool4 layer of VGG-16 and adopt dilation for ResNet. Secondly, we combine the revised architecture of Faster-RCNN with Online Hard Examples Mining (OHEM) to make the system more robust to locate the region of small traffic signs. Finally, we conduct extensive experiments and empirical evaluations on several different videos to demonstrate the satisfying performance of our approach. i.e., the experimental results show our approach improve the mean average precision by 12.1% over the original object detection algorithm. [ABSTRACT FROM AUTHOR]

This study assesses the impact of traffic sign deficit on road traffic accidents in Nigeria. The participants were 720 commercial vehicle drivers. While simple random sampling was used to select 6 out of 137 federal highways, stratified random sampling was used to select six categories of commercial vehicle drivers. The study used qual-dominant mixed methods approach comprising key informant interviews; group interviews; field observation; policy appraisal and secondary literature on traffic signs. Result shows that the failure of government to provide and maintain traffic signs in order to guide road users through the numerous accident black spots on the highways is the major cause of road accidents in Nigeria. The study argues that provision and maintenance of traffic signs present opportunity to promoting safety on the highways and achieving the sustainable development goals. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Traffic & Transportation Research / Trafik ve Ulaşım Araştırmaları Dergisi is the property of Journal of Traffic & Transportation Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic signs plays an important role in pedestrian path guidance in the urban rail transit hub. However, the existing traffic signs within urban rail transit hubs are more or less deployed rigidly, which cannot fully consider the relationship between the layout and visual information about passengers' decisions. Therefore, this paper analyses the influence of traffic signs on the path selection behavior of pedestrians, and construct the MAKLINK diagram to study the impact of traffic signs on pedestrians' proceeding decisions by a simulation technique. With the MAKLINK diagram, the path plan under the guidance of traffic signs is formulated. Then, the optimal pedestrian path on the MAKLINK diagram considering the effect of traffic signs is optimized via the Ant Colony Algorithm. An empirical study at Beijing South Railway Station is conducted. The findings reflect that traffic signs within urban rail transit hub could effectively take effect to the path selection behavior of large-volume passengers. [ABSTRACT FROM AUTHOR]

A group of adult individuals with dyslexia and a matched group of normally reading individuals participated in a driving simulation experiment. Participants were asked to read the word presented on every direction traffic sign encountered along a route, as far as possible from the sign, maintaining driving performance. Word frequency and word length were manipulated as within-subject factors. We analyzed (a) reading accuracy, (b) how far the sign was when the participant started to give the response, (c) where the participant looked during the time leading up to the response, and (d) the variability of the vehicle's speed during that time and during driving on similar segments of the route that did not present the traffic signs. Individuals with dyslexia showed lower levels of performance in the reading task, the roles of word frequency and word length were more influential for them, and there was larger variability of the vehicle's speed during the time they were attempting to read the traffic sign, which did not occur during their driving on similar segments that did not present the targeted traffic signs. Therefore, the specific needs of individuals with dyslexia on the road should be considered in plans aimed at increasing traffic safety and fluidity. [ABSTRACT FROM AUTHOR]

In this study, the authors present a new efficient method based on discriminative patches (d‐patches) for holistic traffic sign detection with occlusion handling. Traffic sign detection is an important part in autonomous driving, but usually hampered by the occlusions encountered on roads. They propose a method which basically upgrades d‐patches by integrating vocabulary learning features. Consequently, d‐patches are more discriminatively trained for robust occlusion handling. In addition, a holistic classifier is trained on d‐patches, which identify those regions where occlusion exists. This results in higher confidence‐score for the regions which contain traffic signs and lower confidence‐score for the regions containing occlusions. Furthermore, they also propose a new coarser‐to‐fine (CTF) approach to speed up the traffic sign detection process. CTF minimises the use of traditional sliding window for object detection. It relies on colour variance to search the regions with high probability of traffic sign presence. Sliding window is used only on the selected high probability regions. The proposed method achieves 100% detection results on German Traffic Sign Detection Benchmark and performs 2.2% better than the previous state‐of‐the‐art methods on Korean Traffic Sign Detection dataset, under partially occluded settings. By using CTF approach, five times speedup with a marginal loss in accuracy can be achieved. [ABSTRACT FROM AUTHOR]