Relevant bibliographies by topics / Traffic signs and signals

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  1. Journal articles

Academic literature on the topic 'Traffic signs and signals'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Traffic signs and signals"

1

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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2

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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4

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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7

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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8

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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9

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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10

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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