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Journal articles on the topic 'Traffic signs and signals'
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Inagaki, Joji. "Traffic message signals and signs." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 76, no. 1 (1992): 21–24. http://dx.doi.org/10.2150/jieij1980.76.1_21.
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Snehal Chaudhary, Et al. "Use of Convolutional Neural Network and SVM Classifiers for Traffic Signals Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 490–93. http://dx.doi.org/10.17762/ijritcc.v11i9.8834.
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Road signals are crucial for preserving a safe and effective flow of traffic. They give directions to cars, warn them of potential dangers, and notify them of the conditions of the road ahead. Road signs make roadways safer for both vehicles and pedestrians by regulating traffic and reducing accidents. Failure to obey traffic signals can be harmful and result in collisions. Drivers must always be conscious of their surroundings and pay attention to traffic signs. If a driver misses a signal, they should proceed with caution and safety to prevent injuring themselves or others, and they should seek assistance to reroute themselves. Through the use of machine learning techniques, this project will create a traffic signal recognition system that will identify the traffic signals that are present on the road and inform the driver if the system determines that the motorist has missed a traffic signal or is thus violating traffic laws.
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Xiong, Jun Yu, Xiao Hui Du, Jia Qi Wang, and Hui Li Zhai. "A Optimized Design of One Traffic Circle." Advanced Materials Research 588-589 (November 2012): 1632–35. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1632.
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In this paper we use queuing theory to analysis the incoming traffic, developed an effective way to control the traffic of a circle by using stop signs and yield signs,and calculated the traffic capacity and average waiting time of this method. Then, we use signals to control the traffic and improve the original method by a analysis the ways the car can pass through the circle crossing. Taking into account of the traffic flow in the different time of a day, we got the light's signal period to adapt to the features of the traffic flow.
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Saadi Abdullah, Ahmed, Majida Ali Abed, and Ahmed Naser Ismael. "Traffic signs recognitionusing cuckoo search algorithm and Curvelettransform with image processing methods." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (2019): 74–81. http://dx.doi.org/10.29304/jqcm.2019.11.2.591.
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Compliance with traffic signs is one of the most important things to follow to avoid traffic accidents as well as compliance with traffic rules in terms of parking, speed control, and other traffic sings. Progress in different areas, such as self-propelled car manufacturing or the production of devices that help the visually impaired, require values to find a way to determine traffic signals with high precision in this research, The first step is to take a picture of the traffic sign and apply some digital image processing techniques to increase image contrast and eliminate noise in the image, the second step resize of origin image , the third step convert color to(YCbCr, HSB) or stay on RGB, the fourth step image is disassembled using curvelet transform and get coefficients , and the last step using cuckoo search algorithm to recognition sings traffics ,the MATLAB (2011b) program was used to implement the proposed algorithm . After applying this method to a set of traffic the percentage of discrimination of traffic signs was yellow 93%, green 94%, blue 94.5%, red 96%.
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C, Bharanidharan, Jeevan Chandra, Hitesh Kumar, Jayasurya s, and Stella A. "GLOBAL IMAGE IDENTIFIER." International Research Journal of Computer Science 9, no. 8 (2022): 195–200. http://dx.doi.org/10.26562/irjcs.2022.v0908.08.
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Many of the things, signs, and symbols we encounter when exploring the world might not be familiar to us. A global image identifier must be created to minimize confusion and misunderstanding. We shall use the less-than-universal traffic signs as an example. Road signs are strategically positioned to safeguard drivers’ and tourists' safety. Additionally, they offer instructions on when and where cars should turn or not turn. The traffic signs on the road express several cautions. In India, there are 400 traffic accidents per day, according to official statistics. Road signs ensure the safety of both automobiles and pedestrians by preventing accidents from occurring. Additionally, traffic signals reduce the incidence of traffic offences by ensuring that drivers follow certain laws. All users of the road, including pedestrians and automobiles, should give priority to traffic signals. For a multitude of reasons, including difficulty focusing, tiredness, and lack of sleep, we fail to see traffic signs. Other reasons for ignoring the indicators include impaired vision, the outside world's influence, and environmental factors. There is a critical need for a system that can recognize traffic lights automatically.
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Gaurav Singh and Prof. Sonam Singh. "Traffic Object Detection and Recognition Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 4 (2024): 81–86. http://dx.doi.org/10.32628/cseit24104110.
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You are already known about automatic vehicles in which the car can control itself. Cars must clearly understand and recognize all traffic signals. Many organizations named Uber, Google, Tesla, Toyota, Mercedes-Benz, Ford, Audi and others are getting involved on this technology to enhance their experience by adding features like autonomous driving and putting efforts in maximum innovation in this field. As a result, if we want to work with this technology accurately it depends on how the vehicle can distinguish between different signs such as no entry, height limit, turning signs, school signs, hospital signs, and many others. Traffic sign recognition is the process of differentiating the traffic signals into similar classes. Here we created a deep-neural-network system that can differentiate traffic signs. Using this system, we can analyze and process different traffic signals which plays a major role in all automatic vehicles. By using CNN, we propose an automated system for traffic sign detection, firstly conversion of original image to grey scale image takes place with the help of some vector machines used there, after that the convolutional-neural network is applied with limited and learnable layer for analyzing. Here it tries to crop the image boundary as per the original have.
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Ford, Garry L., and Dale L. Picha. "Teenage Drivers’ Understanding of Traffic Control Devices." Transportation Research Record: Journal of the Transportation Research Board 1708, no. 1 (2000): 1–11. http://dx.doi.org/10.3141/1708-01.
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Teenage drivers are involved in traffic crashes more often than any other driver group, and their fundamental knowledge of traffic control devices and rules of the road is extremely important in safe driving. Only limited data exist, however, on teenage drivers’ understanding of traffic control devices, and little research has been done on determining their comprehension thereof. Research was performed to document teenage drivers’ ability to understand 53 traffic control devices. These traffic control devices included 6 combinations of sign shape and color; 8 regulatory signs; 14 warning signs; 7 school, highway–railroad grade crossing, and construction warning signs; 7 pavement markings; and 11 traffic signals. Research results were then compared with previous comprehension studies to identify specific traffic control devices that the driving public continually misunderstands. In general, the results indicated that surveyed teenage drivers understood the traffic control devices to some degree. Only nine devices were understood by more than 80 percent of the respondents. The devices found problematic to teenage drivers include combinations of sign shape and color, warning-symbol signs, white pavement markings, flashing intersection beacons, and circular red/green arrow left-turn-signal displays. Recommendations include revising states’ drivers handbooks and increasing emphasis in the driver education curriculum to clarify the meaning and intent of problematic traffic control devices.
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Gore, Shubham, Manan Bhasin, and Suchitra S. "Traffic Sign Detection using Yolo v5." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2679–83. http://dx.doi.org/10.22214/ijraset.2023.51591.
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Abstract: One of the crucial areas of research in the field of advanced driver assistance systems (ADAS) is the detection and recognition of traffic signals in a real-time environment. These are specifically developed to work in real-time to improve road safety by informing the driver of various traffic signals such as speed limits, priorities, restrictions, and so on. This research paper proposes a traffic sign identification system on an Indian dataset utilizing the YOLOv5 model. This study suggests a method for detecting a particular set of 10 traffic signs. You Only Look Once (YOLO) v5 is the algorithm used to detect traffic signs, and the model parameters are trained on train sets obtained from the recently constructed dataset. The remaining images from the dataset are utilized to create a test set. When tested on the test set made from the suggested dataset, the proposed approach for detecting a particular set of traffic signs performs admirably
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Neelima, Vaka, Cherukuri Nayomi, Arla Prasanna Kumari, Munnangi Ravi Teja, Mr K. Sivakrishna, and Dr M. Sreenivasulu. "Traffic Signs Recognization using Machine Learning." International Journal of Innovative Research in Engineering and Management 9, no. 2 (2022): 665–60. http://dx.doi.org/10.55524/ijirem.2022.9.2.105.
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The expansive road network in India is responsible for the movement of the vast majority of the country's products as well as its population. Intelligent transit systems are one example of the cutting-edge technology that has been developed and implemented over the course of the past three decades to enhance the safety of public transportation and reduce emissions. Other examples of this cutting-edge technology include autonomous vehicles and magnetic levitation. (ITS). In spite of the difficulties, there is still a sizeable scholarly community that is interested in researching methods that are predicated on ITS for the purpose of identifying traffic signals. These researchers are trying to figure out how to better collect and analyze impulses, specifically at night or in conditions where there is restricted illumination. Specifically, they are focusing on the nighttime circumstances. The course of this research led to the development of a number of strategies for accelerating the procedures of form model extraction, segmentation, and feature extraction. These strategies were presented throughout the course of the study. When a person has more experience, they should be able to realistically anticipate a higher general rate of accurate identifications.
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Dang, Xiaochao, Wenze Ke, Zhanjun Hao, Peng Jin, Han Deng, and Ying Sheng. "mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud." Sensors 23, no. 15 (2023): 6816. http://dx.doi.org/10.3390/s23156816.
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Automatic driving technology refers to equipment such as vehicle-mounted sensors and computers that are used to navigate and control vehicles autonomously by acquiring external environmental information. To achieve automatic driving, vehicles must be able to perceive the surrounding environment and recognize and understand traffic signs, traffic signals, pedestrians, and other traffic participants, as well as accurately plan and control their path. Recognition of traffic signs and signals is an essential part of automatic driving technology, and gesture recognition is a crucial aspect of traffic-signal recognition. This article introduces mm-TPG, a traffic-police gesture recognition system based on a millimeter-wave point cloud. The system uses a 60 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar as a sensor to achieve high-precision recognition of traffic-police gestures. Initially, a double-threshold filtering algorithm is used to denoise the millimeter-wave raw data, followed by multi-frame synthesis processing of the generated point cloud data and feature extraction using a ResNet18 network. Finally, gated recurrent units are used for classification to enable the recognition of different traffic-police gestures. Experimental results demonstrate that the mm-TPG system has high accuracy and robustness and can effectively recognize traffic-police gestures in complex environments such as varying lighting and weather conditions, providing strong support for traffic safety.
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J, Sanjay, Sandesh Saidapur, Sanjay CR, and Shamanth Reddy. "Traffic Sign Detection using Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4269–73. http://dx.doi.org/10.22214/ijraset.2023.51261.
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Abstract: Signs on the road are crucial making sure the flow of traffic is smooth. Disregard whilst watching the traffic sign board is one of the main causes of traffic accidents. Traffic signs are essential for controlling traffic, preventing accidents, and assuring safety.No entry, speed limit, traffic lights, left or right turn, children crossing, etc. are just a few examples of various sorts of signs. The human observation of traffic control signals under the current approaches could lead to a re-entry collision of cars. The fast-moving traffic may be delayed as a result. Additionally, they impede traffic by stopping cars at the intersection during rush hour. The revised approach uses Deep Learning to reduce the amount of time and effort required to monitor these
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Allen, R. Wade, Zareh Parseghian, and Theodore J. Rosenthal. "Simulator Evaluation of Road Signs and Signals." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 14 (1994): 903–6. http://dx.doi.org/10.1177/154193129403801423.
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This paper describes a accuracy versus speed paradigm for evaluating signing and traffic signal conditions using low cost simulation technology. Two research examples are reviewed. One study involved the use of an interactive driving simulator that included the presentation of high resolution signs over the apparent viewing range from 500 to 50 feet. Drivers had to control vehicle speed and lane position while identifying the meaning of symbol signs as rapidly as possible. Subjects were scored in terms of correctness and the distance at which signs were identified. A second study involved a computer controlled presentation of static signalled intersection scenes, including supplemental signs, to subjects who were required to make decisions about permissive movements. Subjects were required to make decisions about permissive movements as rapidly as possible, and were scored by the computer on correctness and response time. Results in both studies showed that both response speed and correctness degrade with the complexity of signal and sign treatments.
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Almusawi, Husam A., Mohammed Al-Jabali, Amro M. Khaled, Korondi Péter, and Husi Géza. "Self-Driving robotic car utilizing image processing and machine learning." IOP Conference Series: Materials Science and Engineering 1256, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1757-899x/1256/1/012024.
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Abstract The major goal of this paper is to build and represent a prototype of a fully autonomous car that employs computer vision to detect lanes and traffic signs without human intervention using limited computing capacity. The project contains an embedded system represented by a Raspberry Pi 3 which serves as the image processing and machine learning unit. This method requires a stream of images as input for the computer vision using OpenCV2 library with C++ programming language along with Haar Cascade Classifier for the detection of traffic signs. The Raspberry Pi will send binary signals to the Arduino UNO which is responsible for merging those signals with the ones from the ultrasonic sensor and producing new signals which are sent to the motor driver to control the direction and speed of the dc motors. The system was able to detect the lane and respond to changes in lane direction, as well as to detect traffic signs and give appropriate responses.
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Sreenivas, Dr M. "Traffic Sign Recognition Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3522–34. http://dx.doi.org/10.22214/ijraset.2022.44532.
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Abstract: You've probably heard about self-driving automobiles, in which the passenger can completely rely on the vehicle for transportation. Cars must, however, understand and follow all traffic rules in order to achieve level 5 autonomy. Many researchers and large organisations, including as Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, and others, are working on autonomous vehicles and self-driving automobiles in the world of artificial intelligence and technological innovation. As a result, in order for this technology to be accurate, the vehicles must be able to understand traffic signs and make proper decisions. Speed limits, prohibited entry, traffic signals, turn left or right, children crossing, no passing of big trucks, and so on are all examples of traffic signs. Traffic sign classification is the process of determining which class a traffic sign belongs to. In this project, we'll create a deep neural network model that can categorise traffic signals in an image into several groups. Using our model, we can read and understand traffic signs, which is a critical function for all autonomous vehicles. Based on Convolutional Neural Networks, we offer a method for detecting traffic signs (CNN). We employ support vector machines to convert the original image to grey scale, then apply convolutional neural networks with fixed and learnable layers for detection and recognition. The fixed layer can limit the number of interest regions to be detected and crop the boundaries to be as near to the original as possible.
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Lee, Suzanne E., Sarah B. Brown, Miguel A. Perez, Zachary R. Doerzaph, and Vicki L. Neale. "Normal and Hard Braking Behavior at Stop Signs and Traffic Signals." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 22 (2005): 1897–901. http://dx.doi.org/10.1177/154193120504902203.
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A testbed intersection violation warning system was developed to address the problem of intersection crashes. The effectiveness of such systems is fundamentally dependent on the driver-braking model used to decide if a warning should be issued to the driver. If the model is unrealistic, drivers can either be annoyed due to assumed braking levels that are too low, or can be warned too late if braking expectations are too high. Initial algorithm development relied on data from the Collision Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) project. However, it was unknown whether the CAMP data (collected in the presence of stopped lead vehicles) would be applicable to the intersection problem (e.g., will drivers respond similarly to red traffic signals and stopped lead vehicles). Braking profile and performance tests were thus conducted to determine the applicability of the CAMP FCW results to the intersection violation warning.
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Satpute, Ms Bhumika Vasant, Ms Dhanlaxmi Balavant Don, Ms Rakhi Ajaykumar Salave, Ms Abrar Zameer Shaikh, and Prof Akash K. Gunjal. "Intelligent Transportation System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4560–69. http://dx.doi.org/10.22214/ijraset.2022.43354.
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Abstract: People have experienced frequent communication and information exchange in recent years as a result of the proliferation of mobile devices. For example, when people go on vacations, it is common for each person to bring a smart phone with them to get information about nearby attractions. When a user visits a location, the application will provide useful information based on the user's current location preferences and previous visits to locations and their traffic signs. This new feature of map will learn your preferences and will display traffic signs in the area this system would display all traffic signs in and around the city including No Parking, Give Way, Speed Breakers ,Zebra Crossings ,Signals ,Tunnels, Sharp Curves, Speed ,No Overtaking Zones, Accidents Ponds, and Cycle Lanes. The use of popularity based filtering allows users to see all of the traffic signs in the area. Keywords: Traffic signs, Intelligent Transportation
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Wani, Gulzar Ahmad, and Dr Gurinder Kaur Sodhi. "Implementation of Bootstrap Technique in Detection of Road Sign using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 2299–304. http://dx.doi.org/10.22214/ijraset.2022.48460.
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Abstract: Traffic sign recognition is a driver assistance tool that can alert and warn the driver by showing any applicable limitations on the current stretch of road. Such limitations include signs such as 'traffic light approaching' or 'walking crossing.' The present research focuses on identifying Indian road and traffic signs in real time. Real-time footage from a moving automobile is captured by a computerized camera, and genuine traffic signs are retrieved using vision data. The network is divided into three stages: one for identification and the other for classification. The first stage created and constructed hybrid colour edge detection. In stage 2, a new and successful custom feature-based technique is used for the first time in a road sign identification strategy. Finally, a multilayer Convolution Neural Network (CNN) with Graphical User Interface (GUI) is being created to identify and analyse various traffic signals. It's tricky, despite the fact that it's been tested on both traditional and nontraffic signs and passed with flying colours..
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Lengyel, Henrietta, and Zsolt Szalay. "Classification of traffic signal system anomalies for environment tests of autonomous vehicles." Production Engineering Archives 19, no. 19 (2018): 43–47. http://dx.doi.org/10.30657/pea.2018.19.09.
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Abstract In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.
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Kim, Eunjee, Hyorim Kim, Yujin Kwon, and Gwanseob Shin. "Visibility of an in-ground signal when texting while walking." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 1933–37. http://dx.doi.org/10.1177/1071181320641466.
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An increase in pedestrian accidents associated with smartphone use has been one of the main issues in road traffic safety research and administration. Recently, traffic lights and safety signs embedded in the ground have been introduced, but without sufficient scientific consideration. A laboratory experiment evaluated the visibility of an in-ground signal while varying its contrast and position. Twenty-three participants performed a signal detection task when conducting texting while walking on a treadmill. The signals were displayed randomly onto the ground one at a time at three different positions with three different contrasts levels and moved towards a participant. In results, the approaching signals were detected 1.7 m ~ 2.9 m in front of participants, and there were significant differences in the visibility between contrast levels and positions (p<.01). The findings suggest the importance of proper contrast level and placement when installing in- ground signals for improving their visibility by smartphone users.
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Jain, Vaibhav, Tanay ., Saransh Gangele, and K. Kalimuthu. "Driver Assistance System using in-vehicle Traffic Lights and Signs." International Journal of Engineering & Technology 7, no. 2.24 (2018): 527. http://dx.doi.org/10.14419/ijet.v7i2.24.12151.
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In recent years, with the advancement of vehicular communication, it is possible to detect various road signs and provide traffic light information to the driver inside the vehicle with the application of heads-up display (HUD). It detects road signs, does basic classifications and accordingly directs the driver to slow down or stop the vehicle. The vehicle’s heads-up display keeps the driver focused by providing road warnings, speed limit, traffic signals and some vital navigation information in the driver’s line of sight(LOS). This system has 4 phases, Image recognition, wireless communication, obstacle detection and driver mechanism. This system aims to create a prototype of a smart driver assistance system which provides better road traffic and driver’s safety in countries with high traffic congestion where fully automated vehicles cannot function effectively. This system can be easily implemented in real time scenarios to reduce accidents and enhance the convenience of driving.
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Alawaji, Khaldaa, Ramdane Hedjar, and Mansour Zuair. "Traffic Sign Recognition Using Multi-Task Deep Learning for Self-Driving Vehicles." Sensors 24, no. 11 (2024): 3282. http://dx.doi.org/10.3390/s24113282.
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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.
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Mohammed, Mohammed. "Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection." International Journal of Neutrosophic Science 23, no. 4 (2024): 29–40. http://dx.doi.org/10.54216/ijns.230403.
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Traffic signs detection is a critical function of automatic driving and assisted driving is a significant part of Cooperative Intelligent Transport Systems (CITS). The drivers can obtain the data attained via automated traffic sign detection to improve the comfort and security of motor vehicle driving and regulate the behaviors of drivers. Recently, deep learning (DL) has been utilized in the fields of traffic sign detection and achieve better results. But there are two major problems in traffic sign recognition to be immediately resolved. Some false sign is always detected due to the interference caused by bad weather, and illumination variation. Some traffic signs of smaller size are increasingly complex to identify than larger size hence the smaller traffic signs go unnoticed. The objective is to achieve the accuracy and robustness of traffic sign detection for detecting smaller traffic signs in a complex environment. Thus, the study presents a Golden Jackal Optimization with Neutrosophic Rule-Based Classification System (GJO-NRCS) technique for Enhanced Traffic Sign Detection. The GJO-NRCS technique aims to detect the presence of distinct types of traffic signs. In the GJO-NRCS technique, DenseNet201 model is exploited for feature extraction process and the GJO algorithm is used for hyperparameter tuning process. For final recognition of traffic signals, the GJO-NRCS technique applies NRCS technique. The simulation values of the GJO-NRCS method can be examined using benchmark dataset. The experimental results inferred that the GJO-NRCS method reaches high efficiency than other techniques.
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Koh, Dong-Woo, Jin-Kook Kwon, and Sang-Goog Lee. "Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals." Sensors 21, no. 13 (2021): 4607. http://dx.doi.org/10.3390/s21134607.
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Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.
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Balázs, Viktor, László Szilágyi, Antal Apagyi, and Timotei István Erdei. "The Implementation of an Opencv-Based Traffic Sign Identifier Videoanalyst Software." Műszaki Tudományos Közlemények 9, no. 1 (2018): 39–42. http://dx.doi.org/10.33894/mtk-2018.09.05.
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Abstract Nowdays, accidents tend to happen because our attention is being split up by the ever-growing influx of information, losing the focus from the driving, traffic signs, and other signals. The consequences of these minor or major accidents weight down on our shoulders. During our project, we tried to eliminate, or help this issue, using present technology, improving upon that, trying to avoid these accidents. Our task consisted on implementing a software, that could identify traffic signs from any video streams.
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Kozin, Yuriy. "Road Traffic Light in New Configuration." Journal of Road Safety 32, no. 1 (2021): 52–54. http://dx.doi.org/10.33492/jrs-d-20-00253.
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The three-color system containing signals of the same circular shape has been in existence for over a hundred years. Each traffic signal has been justifiably selected to have a special color light to correspond to human psychoemotional reaction (red – stop, yellow – caution, green – go) to a given color signal (British Standards, 2015) and to comply with the laws of physics (The Motivated Engineer, 2015) – Rayleigh’s scattering law (Banc SpaceTek, 2017). The main downsides of the traditional road traffic light include the following: • The uniform circular shape of light signals results in uncertainty and difficulties for road users with color blindness and visual impairments, resulting in the need for restrictions or bans on driving license issuance in some countries. This uncertainty becomes particularly acute in conditions of low visibility. • According to the concept of harmony of form and color (Itten, 1961), a green light alone corresponds to the circular (spherical-like) shape of the signal. Red and amber lights harmoniously combine with other geometrical shapes. • The uniform shape of light signals prevents the implementation of the original compact combined model of traffic lights. For example, during the day, colorblind people can tell which signal is which because there is a standard position assigned: top – bottom or right – left (Oliveira, Souza, Junior, Sales & Ferraz, 2015). This becomes problematic if the compact combined models of traffic lights are used. Engineers and inventors have been trying to solve these problems by introducing random changes in the light signal shape and complicating the traffic light design. For a long time there have been different proposals about how to eliminate the demerits of the existing traffic lights: from arbitrary changes in the signal shape (Patterson, 1988) to transformation of traffic lights into a single-section display panel (Kulichenko, 2011) which replaces among others stationary road signs. However, technical solutions like these deprive the traffic light of its signal uniformity and conciseness (simplicity, clarity and precision of its controlling effect), features which help safe traffic regulation in a busy and dynamic mode. Technical modernization of individual signal components has been going hand in hand with technological developments as light sources, diffusers, lenses, controllers, materials, control systems, timers, etc. are improved. However, adequate design and aesthetic proposals are considerably behind. The aim of this paper is to propose a concept of creating control signals of traffic light that harmonize color and form, and, as a result, to create a new model of traffic light that will be convenient for all road users.
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Jiang, Wei. "A traffic and road signal recognition method through deep learning." Applied and Computational Engineering 76, no. 1 (2024): 280–87. http://dx.doi.org/10.54254/2755-2721/76/20240617.
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Before deep learning became popular, some researchers used traditional computer vision algorithms, including support vector machines, decision trees, and random forests to deal with traffic and road signal recognition problems. These methods often require manual design of features and may not perform well when dealing with complex scenarios and changing conditions. Therefore, traffic and road signal recognition have witnessed a transformative shift with the advent of deep learning technologies. Convolutional neural networks (CNNs) developed from deep learning has shown prominent capabilities in accurately detecting and interpreting traffic signs and signals from images and videos. In this project, the whole method is mainly based on two parts, which refers to the process of signal-capturing and the detecting process. The CNN model used in the latter part consists of various function layers, including max-pooling layer, linear layer, 2D convolution layer and batch- normalization layer to modify accuracy. This model is trained and tested with Chinese Traffic Sign Dataset, with 58 categories of over 6000 pictures. With the assistance of OpenCV, the model may detect real-time traffic signals and convey messages to vehicles. After adjusting parameters in the CNN model, high probability tags are obtained during the process, with both accuracy and average loss labeled after each epoch. The results demonstrate that the proposed model achieved the testing accuracy of over 95 percent, after undergoing 200 epochs.
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Kharchenko, I. K., I. G. Borovskoy, and E. А. Shelmina. "Modular Architecture of Advanced Driver Assistance Systems for Effective Traffic Sign Recognition." Vestnik NSU. Series: Information Technologies 21, no. 3 (2023): 56–71. http://dx.doi.org/10.25205/1818-7900-2023-21-3-56-71.
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Analysis of modern approaches to the implementation of driver assistance systems, as well as the implementation of the architecture of the driver assistance system, aimed at recognizing traffic signs at the maximum distance from it under difficult weather conditions, for early feedback to the driver. The paper considers the main signals used in the implementation and operation of the driver assistance system: data from the car's CAN bus, information from a GPS receiver, video fragments from a digital camera. The presented modular architecture uses the listed data sources for estimating the traffic situation, as well as neural network methods for recognizing traffic signs. The modular architecture of the driver assistance system is presented, which allows notifying the driver about traffic signs. The system is equipped with lane boundary control to alert the driver to signs related to the adjacent carriageway when turning. It has been experimentally proven that the modular architecture of the driver assistance system presented in the paper is not inferior in speed and accuracy to alternative systems, acting as a comprehensive autonomous solution.
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Paavani, R. Krishna, V. Indraja, V. Neelimajyothi, S. Sai, and Mr M. Sriramulu. "Traffic Sign Board Detection Using Single Shot Detection (SSD)." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4095–97. http://dx.doi.org/10.22214/ijraset.2022.43336.
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Abstract: Traffic sign board detection (TSBD) is a significant portion of intelligent transportation system (ITS). Being able to identify traffic signals more accurately and effectively can improve safe driving .Due to increase in technology there are autonomous vehicles . The traffic sign recognition process includes two parts: detection and classification. In this paper, we use an object detection algorithm called SSD to detect the traffic signs. This convolutional neural network uses multiple feature maps to detect objects. For the traffic sign is very small to the whole picture, the SSD model has been improved to have a better detection result of traffic signs. In the experiments, the model has been simplified and the size of the prior box has been modified. The improved network has a good detection effect on small targets. The results on the test data set show that the proposed algorithm performs well for single-target, multi-target and dark-light images. The precision and recall on the test data set are 91.09%, and 88.06%. Keywords: Automatic traffic sign board Detection, SSD, Image processing, Text alert.
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Domínguez, Hugo, Alberto Morcillo, Mario Soilán, and Diego González-Aguilera. "Automatic Recognition and Geolocation of Vertical Traffic Signs Based on Artificial Intelligence Using a Low-Cost Mapping Mobile System." Infrastructures 7, no. 10 (2022): 133. http://dx.doi.org/10.3390/infrastructures7100133.
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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.
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A, Jayaprakash, and C. Kezi Selva Vijila. "Detection and Recognition of Traffic Sign using FCM with SVM." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 6 (2017): 6285–89. http://dx.doi.org/10.24297/jac.v13i6.5773.
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This paper mainly focuses on Traffic Sign and board Detection systems that have been placed on roads and highway. This system aims to deal with real-time traffic sign and traffic board recognition, i.e. localizing what type of traffic sign and traffic board are appears in which area of an input image at a fast processing time. Our detection module is based on proposed extraction and classification of traffic signs built upon a color probability model using HAAR feature Extraction and color Histogram of Orientated Gradients (HOG).HOG technique is used to convert original image into gray color then applies RGB for foreground. Then the Support Vector Machine (SVM) fetches the object from the above result and compares with database. At the same time Fuzzy Cmeans cluster (FCM) technique get the same output from above result and then to compare with the database images. By using this method, accuracy of identifying the signs could be improved. Also the dynamic updating of new signals can be done. The goal of this work is to provide optimized prediction on the given sign.
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Azzam, Diya Mahmoud, and Craig C. Menzemer. "Numerical Study of Stiffened Socket Connections for Highway Signs, Traffic Signals, and Luminaire Structures." Journal of Structural Engineering 134, no. 2 (2008): 173–80. http://dx.doi.org/10.1061/(asce)0733-9445(2008)134:2(173).
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Douville, Phil. "Real-Time Classification of Traffic Signs." Real-Time Imaging 6, no. 3 (2000): 185–93. http://dx.doi.org/10.1006/rtim.1998.0142.
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Mitroshin, Dmitriy V. "On the Improvement of the International Statutory Regulation in Road Traffic." Administrative law and procedure 2 (February 11, 2021): 25–28. http://dx.doi.org/10.18572/2071-1166-2021-2-25-28.
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The article describes the role of the Russian Federation in the development of an international legal framework for road traffic, and its implementation at the global and regional levels. The content of the amendments implemented in the relevant basic international legal acts — the 1968 Conventions on Road Traffic and on Road Signs and Signals was specified. An assessment is given to the contribution of LL.D, Professor Alexander Yuryevich Yakimov to this activity.
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Korniienko, V., O. Gerasina, D. Tymofieiev, O. Safarov, and Y. Kovalova. "Models of monitoring of self-like traffic of information and communication networks for attack detection systems." System technologies 6, no. 137 (2021): 99–113. http://dx.doi.org/10.34185/1562-9945-6-137-2021-10.
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Autoregressive, fractal and multifractal models of network self-similar traffic are con-sidered, which allow to form an adequate reference model (template) of "normal" traffic and to detect traffic anomalies in attack detection and prevention systems. Models of fractal Brownian motion and fractal Gaussian noise were considered as models of fractal motions, because they have self-similarity and long-term dependence properties that correspond to the properties of experimental data, as well as the possibility of their analytical interpretation. When evaluating and identifying processes for the implementation of autoregressive models use adaptive filters-approximators, among which there are neural network and neuro-wavelet. The following were used as multifractal models: a multifractal wavelet model with a beta distribution and a hybrid multifractal wavelet model in which the beta distribution is used on a coarse scale and the dis-tribution of point masses on an accurate scale By modeling as a result of adaptation and learning of models, autocorrelation functions, spectra and variances of model signals qualitatively correspond to the graphs of the experimental signal. In addition, the qualitative and numerical values of the characteristics of the model signals generally correspond to the characteristics of the experimental signal. In this case, beta multifractal wavelet models have a smaller error of determination of characteristics than hybrid multifractal wavelet models, and the relative root mean square error of approximation of the experimental signal using a neural network adaptive filter approximator does not exceed 0.046. Statistical verification by non-parametric criterion of signs allowed to establish the adequacy of experimental and model signals with a significance level of 0.01. Further research should be aimed at developing and using predictive models of self-similar traffic in attack detection and prevention systems, which will increase the efficiency of attack detection.
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Zheng, Meizhu, Yanzhi Zhang, Haiyang Lv, and Chuan Xiao. "Front-End Circuit for Photomultiplier Tube Signal Readout Based on Recognition of Traffic Signal Images." Journal of Nanoelectronics and Optoelectronics 18, no. 11 (2023): 1366–73. http://dx.doi.org/10.1166/jno.2023.3516.
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Photoelectric sensing technology plays a crucial role in vehicular equipment, which is equipped with various photoelectric devices to perceive the surrounding environment and avoid traffic lights and vehicles. This research selects the Hamamatsu H9500, a 256-channel, position-sensitive photomultiplier tube, as the test unit. It aims to simplify signal readout while improving the spatial resolution of the photodetector. This research focuses on designing a charge distribution circuit named Discretized Positioning Circuit (DPC) for the photomultiplier tube, with an additional charge-sensitive front-end amplification and shaping circuit. This circuit can convert the weak current signals from the H9500 into voltage signals. The shaping part of the circuit employs an active CR-RC circuit with weak signal amplification capabilities. This circuit is deployed within the photomultiplier tube, strategically positioned on vehicles to recognize various traffic sign images. The front-end shaping circuit is tested in the experiments, which converts square wave voltage into pulse current using a capacitor. It is observed that the current signal has a certain width and the voltage waveform of the CR differential circuit can be obtained by increasing the input impedance to 1 MΩ. During input voltage amplitude testing, the corrected output signal voltage shows a good linear relationship with the input square wave voltage. This designed front-end shaping circuit is used for signal readout in photomultiplier tubes and deployed in vehicular equipment to collect image information of traffic signs. After image processing, satisfactory recognition results are achieved.
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Venkatramulu, S., Bairy Yugasri, Triveni Mohan Sadala, Garidepalli Revathi, and V. Chandra Shekhar Rao. "Violation of Traffic Rules and Detection of Sign Boards." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 249–55. http://dx.doi.org/10.17762/ijritcc.v11i8s.7204.
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Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used.
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Balado, Jesús, Elena González, Pedro Arias, and David Castro. "Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning." Remote Sensing 12, no. 3 (2020): 442. http://dx.doi.org/10.3390/rs12030442.
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Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.
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Hashim, Rabia, Ravinder Pal Singh, and Monika Mehra. "Road Sign Detection System using Neural Networks and Tensor Flow." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 548–56. http://dx.doi.org/10.22214/ijraset.2022.40672.
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Abstract: Automated tasks have simplified almost everything we perform in today's environment. Due to a desire to focus only on driving, drivers regularly ignore signs placed on the side of the road, which can be harmful to themselves and others. To address this issue, the motorist should be informed in a method that does not require them to divert their concentration. Traffic Sign Detection and Recognition (TSDR) is critical in this case since it alerts the motorist of approaching signals. Not only are roads safer because of this, but motorists also feel more at ease when driving unfamiliar or difficult routes. Another typical issue is inability to read the sign. Driver assistance systems (ADAS) will make it easier for motorists to read traffic signs with the help of this software. We provide a traffic sign detection and recognition system that employs image processing for sign detection and an ensemble of Convolutional Neural Networks (CNNs) for sign recognition. Because of its high recognition rate, CNNs may be used in a wide range of computer vision applications. TensorFlow is used in CNNTSR (Traffic Sign Recognition), a key component of current driving assistance systems that improves driver safety and comfort. TensorFlow is used to implement CNNTSR (Traffic Sign Recognition). This article examines a technology that assists drivers in recognizing traffic signs and avoiding road accidents. Two things determine the accuracy of TSR: the feature extractor and the classifier. Although there are a variety of approaches, most recent algorithms use CNN (Convolutional Neural Network) to do both feature extraction and classification tasks. Using TensorFlow and CNN, we create traffic sign recognition. The CNN will be trained using a dataset of 43 distinct types of traffic signs. The accuracy of the findings will be 95 percent. Keywords: Driver, Tensor flow, Data Sheet, Alert, CNNTSR, ADAS.
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Hololobova, Oksana, Serhii Buriak, Volodymyr Havryliuk, Ihor Skovron, and Oleksii Nazarov. "Mathematical modelling of the communication channel between the rail circuit and the inputs devices of automatic locomotive signalization." MATEC Web of Conferences 294 (2019): 03009. http://dx.doi.org/10.1051/matecconf/201929403009.
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In modern practice of operating under traffic safety conditions, the traffic light signal must be transmitted to the locomotive that moves to it, and duplicate in the driver’s cab. However, this communication channel is not protected from external interference. In order to prevent the occurrence of code failure, it is necessary to create conditions under which the automatic locomotive signalling system will distinguish between signals with useful information, from signals with false information. The best way to solve this problem at the first stage is to model the devices. Using the simulation tools of graphical environment of simulation modelling Simulink from Matlab software environment, the software model of the communication channel between the railroad and the input devices of automatic locomotive signalling system was constructed. The created mathematical model with the actual parameters allows us to obtain diagnostic signs of a proper condition, on the basis of which the research is aimed at the identification, recognition and definition of various types of malfunctions, failure, damages and defects in the work of the constituent elements of the system and the signal transmission channel of the automatic locomotive signalling system.
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Et. al., Nikhil S. Rajguru,. "Implementation paper of Traffic Signal Detection and Recognition using deep learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 212–19. http://dx.doi.org/10.17762/turcomat.v12i1s.1760.
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Traffic boards and traffic signals are used to maintain proper traffic through busy roads. They help to recognize the rules to follow when driving the vehicle. These signs warn the distracted driver, and prevent his/her actions which could lead to an accident. We have proposed a system which can help recognize these boards and signals at real time thus avoiding major mishap. A real-time automatic sign detection and recognition can help the driver, significantly increasing his/her safety. Lately traffic sign recognition has got an immense interest lately by large scale companies such as Google, Apple and Volkswagen etc. which is driven by the market needs for intelligent applications such as autonomous driving, driver assistance systems (ADAS), mobile mapping, Mobil eye, Apple, etc. Hence, here, we have implemented to do the same with cost efficient manner using Raspberry Pi. The proposed system detects the traffic board or traffic signals, capture its image which through deep learning approach recognizes the same to give result on dashboard as well it gives the measures of distance from front obstacle which helps to implement brake system if obstacle is near. PiCam is used to capture images of traffic sings and is connected to RaspberryPi. Monitor is used to display required output, showing type of sign and distance of collision. This proposal will avoid large number of accidents occurring at bridges and work in progress area due to automated braking system and simultaneous reduce death ratio.
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Varhokar, Anuj A., and P. J. Wadhai. "A Traffic Volume Study for Efficient Roundabout Planning." IOP Conference Series: Earth and Environmental Science 1326, no. 1 (2024): 012112. http://dx.doi.org/10.1088/1755-1315/1326/1/012112.
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Abstract Roundabouts are intersections of two or more roads with one-way circulating streets that give oncoming traffic priority. Traffic signs control the approaching traffic, which is only allowed to turn left onto the circulating headway. Upon approaching the yield line, the driver must only decide whether there is enough room for them to enter the gap in the moving traffic. The vehicles then effortlessly turn left and leave the circulating route to travel to their final destination. Signal-controlled intersections cause traffic to build up on both sides of the road, whereas roundabout-controlled intersections allow for a smooth flow of vehicles per hour. Due to stops and collisions, traffic signals cause traffic to build up on either side of the signal, causing basic wear and tear. Additionally, fuel is wasted while waiting for the light to turn green. In this paper, the planning of roundabouts will be briefly discussed at Teenbatti junction using the traffic volume study as per IRC recommendations and suggest the necessary measures that needed to be taken immediately to avoid the issue of queuing at Walkeshwar Road due to turning movement of vehicles from Malabar hill to Grant road. The main objective of the study is to address the issue of traffic congestion and queuing at Teenbatti junction by introducing roundabout. It has been concluded that the installation of roundabout and the measures discussed to counteract the various traffic related issues minimizes the traffic congestion as it avails free flow in circular motion to leave the rotary.
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Özarpa, C., İ. Avcı, B. F. Kınacı, S. Arapoğlu, and S. A. Kara. "CYBER ATTACKS ON SCADA BASED TRAFFIC LIGHT CONTROL SYSTEMS IN THE SMART CITIES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 411–15. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-411-2021.
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Abstract. There are regular developments and changes in cities. Developments in cities have affected transportation, and traffic control tools have changed. Traffic signs and traffic lights have been used to direct pedestrians and vehicles correctly. Traffic light control systems are used to ensure the safety of vehicles and pedestrians, increase the fluency in traffic, guide them in transportation, warn pedestrians and drivers, and regulate and control transportation disruptions. In order to facilitate people's lives, it is desired to control the traffic components autonomously with the developments in autonomous systems. Cyber threats arise due to the active use of the internet and signals or frequencies in the use of modules that will provide communication with traffic lights, traffic signs, and vehicles, which are traffic components at the inter-sections of many roads in the control of central systems. The study is limited to smart traffic lights, which are traffic components. If we examine the cyber-attacks, we can see that Malware Attacks, Buffer Overflow Attacks, DoS attacks, and Jamming Attacks can be made. Network-Based Intrusion Detection Systems and Host-Based Intrusion Detection Systems can be used to detect and stop Malware Attacks, Buffer Overflow Attacks, DoS attacks, and Jamming Attacks. Intrusion detection systems tell us whether the data poses a threat or does not pose after the data passing through the system is examined. In this way, system protection is ensured by controlling the data traffic in the system.
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Kozłowska, Małgorzata Klaudia. "Consistency and certainty of the road marking system as a subject of protection based on the offence law. Analysis of the characteristics of the offence from article 85 § 1 of offence code." Transportation Overview - Przeglad Komunikacyjny 2017, no. 1 (2017): 17–23. http://dx.doi.org/10.35117/a_eng_17_01_03.
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Nowadays when the road infrastructure rapidly expands as well as the traffic, the correct road markings are of a vital importance in ensuring safety and efficiency of this traffic. Negligible number of road incidents caused by incorrect road markings results in treating quality and certainty of those markings as being of less importance. Thus, such an important issue is to ensure effective, criminal law protection of the legal interests which is a stable and reliable system of road markings. Polish legislator adopted as a subject of individual protection on the basis of code of offence inviolability of road signs and signals, and what stands behind it - stability and certainty of the road markings system. Road markings; Inviolability of road marks and signals; Road infrastructure
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Hunter, William W. "Evaluation of Innovative Bike-Box Application in Eugene, Oregon." Transportation Research Record: Journal of the Transportation Research Board 1705, no. 1 (2000): 99–106. http://dx.doi.org/10.3141/1705-15.
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An innovative “bike box”—a right-angle extension to a bike lane (BL) at the head of the intersection—was installed with accompanying traffic signs but no extra traffic signals at a busy downtown intersection featuring two one-way streets in Eugene, Oregon, in summer 1998. The box allows bicyclists traveling to the intersection in a left side BL to get to the head of the traffic queue on a red traffic signal indication and then proceed ahead of motor vehicle traffic toward a right side BL when the traffic signal changes to green. Cyclists traveling through the intersection were videotaped before and after placement of the box. The videotapes were coded to evaluate operational behaviors and conflicts with motorists, other bicyclists, and pedestrians. Twenty-two percent of the bicyclists who approached in the left side BL and then crossed to the BL on the right side of the street (the bicyclists for whom the box was most intended) used the box. Many more bicyclists in this target group could have used the box (i.e., they had a red signal indication and enough time to move into the box). A problem with motor vehicle encroachments into the box likely diminished the frequency of use. The rate of conflicts between bicycles and motor vehicles changed little in the before and after periods. No conflicts took place while the bike box was being used as intended.
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Mounika, Podila, and Mudrakola Bhavani. "Traffic Sign Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4965–70. http://dx.doi.org/10.22214/ijraset.2023.54223.
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Abstract: Consensus on the signs and symptoms of the visitor with an understanding of water for humans is fundamentally established. But it is still difficult to identify the signs and symptoms of the guest for the laptop. Both image processing and machine learning algorithms are constantly being developed to better solve this problem. However, with the increase in patient symptoms and the diversity of symptoms, many educational materials are considered to be of limited value. So, how to use smalllabel visitor information to generate good visitor signal (TSR) version information of delivered goods IoT-based (tag all IOTbased). has been an urgent research target. Here we propose a unique method of semi-supervised learning that combines the capabilities of global and local TSR in an IoT-based full-load system. Directional gradient histograms, colouring histograms (CH), and feature capacity (EF) are used to create a custom feature space. Also, in unlabelled models, the fused trait region is thought to reduce the variation of the unique trait. A comprehensive analysis of signs and symptoms from the German Traffic Sign Recognition Benchmark (GTSRB) dataset shows that the proposed method outperforms other methods and provides a potential response to required design practices.
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Hudák, Martin, and Radovan Madleňák. "THE RESEARCH OF DRIVER’S GAZE AT THE TRAFFIC SIGNS." CBU International Conference Proceedings 4 (September 17, 2016): 896–99. http://dx.doi.org/10.12955/cbup.v4.870.
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Traffic signs provide drivers with appropriate warnings and information and signal legal requirements and directions. The aim of this article is to research the frequency and duration of a driver’s gaze at traffic signs. The selected stretch of road in the Slovak Republic has been subjected to a high number of traffic accidents with the most common causes reported as incorrect driver behavior and distracted driving. Therefore, the study’s objective is to measure the time drivers spend looking at billboards. To achieve this outcome, the study uses eye tracking glasses, which are designed to record a person’s natural gaze behavior in real-time. Previous research has shown that the average time gazing at a billboard is 0.543 seconds. The article also contains a comparison of driver’s gaze at different traffic signs and billboards. The economic quantification of traffic accidents on the selected road is also included in the article.
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Long, Richard G., David A. Guth, Daniel H. Ashmead, Robert Wall Emerson, and Paul E. Ponchillia. "Modern Roundabouts: Access by Pedestrians who are Blind." Journal of Visual Impairment & Blindness 99, no. 10 (2005): 611–21. http://dx.doi.org/10.1177/0145482x0509901005.
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This article describes the key differences between roundabouts and traditional intersections that have traffic signals or stop signs and discusses how these differences may affect the mobility of pedestrians who are visually impaired. It also provides a brief summary of the authors’ research on this topic and suggests strategies for addressing the access issues that roundabouts sometimes create.
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Zhang, Furao, Jianan Zhang, Zhihong Xu, Jie Tang, Peiyu Jiang, and Ruofei Zhong. "Extracting Traffic Signage by Combining Point Clouds and Images." Sensors 23, no. 4 (2023): 2262. http://dx.doi.org/10.3390/s23042262.
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Recognizing traffic signs is key to achieving safe automatic driving. With the decreasing cost of LiDAR, the accurate extraction of traffic signs using point cloud data has received wide attention. In this study, we propose combining point cloud and image traffic sign extraction: firstly, we use the improved YoloV3 model to detect traffic signs in panoramic images. The specific improvement is that the convolution block attention module is added to the algorithm framework, the traditional K-means clustering algorithm is improved, and Focal Loss is introduced as the loss function. It shows higher accuracy on the TT100K dataset, with a 1.4% improvement in accuracy compared to the previous YoloV3. Then, the point cloud of the area where the traffic sign is located is extracted by combining the image detection results. On this basis, the outline of the traffic sign is accurately extracted using the reflection intensity, spatial geometry and other information. Compared with the traditional method, the proposed method can effectively reduce the missed detection rate, narrow the range of point cloud, and improve the detection accuracy by 10.2%.
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Naranjo, Manuel, Diego Fuentes, Elena Muelas, et al. "Object Detection-Based System for Traffic Signs on Drone-Captured Images." Drones 7, no. 2 (2023): 112. http://dx.doi.org/10.3390/drones7020112.
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The construction industry is on the path to digital transformation. One of the main challenges in this process is inspecting, assessing, and maintaining civil infrastructures and construction elements. However, Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) can support the tedious and time-consuming work inspection processes. This article presents an innovative object detection-based system which enables the detection and geo-referencing of different traffic signs from RGB images captured by a drone’s onboard camera, thus improving the realization of road element inventories in civil infrastructures. The computer vision component follows the typical methodology for a deep-learning-based SW: dataset creation, election and training of the most accurate object detection model, and testing. The result is the creation of a new dataset with a wider variety of traffic signs and an object detection-based system using Faster R-CNN to enable the detection and geo-location of traffic signs from drone-captured images. Despite some significant challenges, such as the lack of drone-captured images with labeled traffic signs and the imbalance in the number of images for traffic signal detection, the computer vision component allows for the accurate detection of traffic signs from UAV images.
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Poku, Samuel, Delia Bandoh, Ernest Kenu, Emma Kploanyi, and Adolphina Addo- Lartey. "Factors contributing to road crashes among commercial vehicle drivers in the Kintampo North Municipality, Ghana in 2017." Ghana Medical Journal 54, no. 3 (2020): 132–39. http://dx.doi.org/10.4314/gmj.v54i3.2.
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Objective: The study assessed driver, vehicular and road-related factors associated with road crashes (RC) in the Kintampo North Municipality.Design: Cross-sectional studySetting: Kintampo North MunicipalityData source: Demographics, vehicular and road usage information on registered drivers at Ghana Private Road and Transport Union (GPRTU) and Progressive Transport Owners Association (PROTOA) in Kintampo North MunicipalityMain outcome: involvement in road crashes and related factorsResult: A total of 227 drivers were approached for this study. None of them declined participation. They were all males. Most were between 28-37 years (30%). The proportion of drivers that reported RC ever involvement in at least one RC was 55.5% (95% CI: 8.0%, 62.1%). In the bivariate analysis, drink and drive changed lane without signalling, ever bribed police officer, drove beyond the maximum speed limit, paid a bribe at DVLA for driving license, violation of traffic signals were found to be associated with RC involvement (p<0.05). Drivers who violated traffic signals had 2.84 odds of being involved in road crashes compared to those who did not [aOR; 2.84 (95%CI:1.06,7.63)]Conclusion: The proportion of drivers ever involved in road crashes was high. The major factor that is associated with RC involvement was a violation of the traffic light signals. Continuous driver education and enforcement of road traffic regulations by the appropriate authorities could curb the road crash menace in the Municipality.Keywords: commercial drivers, road crashes, vehicle, road signs, traffic light signalFunding: The authors funded this work.