scholarly journals Study on the Deocclusion of the Visibility Window of Traffic Signs on a Curved Highway

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jian Xiao ◽  
Jian Zhao ◽  
Liulin Yang ◽  
Juanxia He ◽  
Yu Li ◽  
...  
Keyword(s):  
Crucial Role ◽  
Visual Recognition ◽  
Driving Safety ◽  
Complex Topography ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Design Formula ◽  
Horizontal Alignment ◽  
Visual Occlusion

Highway navigation is often affected by complex topography, and the flat curve plays an important role in the horizontal alignment design of a highway. Many curves are formed, where visibility could be decreased. Thus, the indicative function of a traffic sign plays a crucial role in ensuring driving safety at the curve. Due to the blocked visibility, the probability of the traffic sign occlusion at the curve of operating highways is quite high. It is urgent to consider the clearing obstructions around traffic signs at curves during highway construction. In this study, the potential of visual occlusion for traffic signs on curved highways was investigated. Firstly, the driver’s visibility window that contains traffic signs was defined and criteria of visual occlusion were proposed. Secondly, a geometric occlusion design formula was established to mimic the visual recognition process of traffic signs on a curved highway, yielding the formula to calculate the visibility window. Finally, the occlusion design formula was applied into a case study of the Beijing-Hong Kong-Macau Expressway (Hunan section), in which visibility windows were calculated and analyzed. The obtained results verified the correctness and effectiveness of the occlusion design formula developed in this study.

scholarly journals The Relationship of the Information Quantity of Urban Roadside Traffic Signs and Drivers’ Visibility Based on Information Transmission

2021 ◽  
Vol 18 (20) ◽  
pp. 10976
Author(s):  
Kun Liu ◽  
Hongxing Deng
Keyword(s):  
Visual Recognition ◽  
Information Overload ◽  
Road Traffic ◽  
Influence Factors ◽  
Urban Traffic ◽  
Traffic Sign ◽  
Information Quantity ◽  
Traffic Signs ◽  
Experienced Driver ◽  
The Relationship

For the lack of quantitative basis of traffic sign combination information, this paper established a model of information quantity of urban road traffic signs by analyzing the driver’s information processing and the visual recognition of traffic signs combined with theories of informatics. It used various analytical methods to build a model of the relationship between the traffic sign information quantity (TSIQ) and the driver’s visual recognition. Based on factors, the relationship between the TSIQ and the driver’s visual recognition was studied and analyzed to provide a reference for the design of urban traffic sign layout information. The results show that the TSIQ can explain 61% of the driver’s recognition time (DRT). The more information the road traffic sign conveys, the longer DRT will be. The TSIQ’s threshold is 642 bits, and exceeding this value will cause information overload. Different influence factors have a certain impact on drivers’ visual recognition distance (VRD). The male VRD is shorter than the female. The VRD of the young driver is larger than the old driver. The VRD of a novice driver is longer than an experienced driver, while the visual recognition sign of an experienced driver is shorter.

scholarly journals MENGENALI RAMBU LALU LINTAS MENGGUNAKAN METODE HOG DAN KNN

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Agnes Dirgahayu Palit
Keyword(s):  
Road Safety ◽  
Performance Testing ◽  
Driving Safety ◽  
Traffic Sign Recognition ◽  
K Nearest Neighbor ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Histogram Of Oriented Gradient ◽  
Sign Recognition ◽  
On The Road

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk membantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Recognition (Sistem Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan metode Histogram of Oriented Gradient (HOG), untuk ektraksi ciri citra, dan K-Nearest Neighbour (KNN) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mengenali citra yang merupakan objek rambu lalu lintas Serta bagaimana performansi akurasi sistem. Diharapkan dengan adanya paduan metode-metode tersebut dapat dilihat bagaimana sistem merekognisi rambu lalu lintas. Dari hasil pengujian performansi sistem dengan nilai k=3, diperoleh akurasi sistem 79.4444 %.Kata kunci : ekstraksi ciri, klasifikasi, HOG, KNN.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs designed to help drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Recognition. The system uses a method Histogram of Oriented Gradient (HOG), for the feature extraction of image characteristics, and K-Nearest Neighbor (KNN) to classify traffic signs image. So, it can be analyzed how the system can detect and recognize the image which is the object of traffic signs And how the accuracy of the system performance. Expected by the blend of these methods can be seen how the system detects and merekognisi traffic signs. From the results of performance testing system with a value of k = 3, acquired 79.4444% system accuracy.

Driver Identification of Landmarks and Traffic Signs after a Stroke

2005 ◽  
Vol 1922 (1) ◽  
pp. 9-14
Author(s):  
Ergun Y. Uc ◽  
Matthew Rizzo ◽  
Steven W. Anderson ◽  
Qian Shi ◽  
Jeffrey D. Dawson
Keyword(s):  
Visual Search ◽  
Target Identification ◽  
Driving Safety ◽  
Cognitive Resources ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Neurologically Normal ◽  
Driver Identification ◽  
Search And Recognition ◽  
Visual Cognitive

A study was done to assess the ability for visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with stroke, that is, drivers who have had a stroke. Visual search for roadside targets during automobile driving can compete for a driver's cognitive resources and may impair driving, especially in drivers with cognitive impairment caused by stroke. Thirty-two drivers with stroke and 137 neurologically normal older adults underwent a battery of visual, cognitive, and motor tests and were asked to report sightings of specific landmarks and traffic signs along a segment of an experimental drive. The drivers with stroke identified significantly fewer landmarks and traffic signs and showed a tendency to make more at-fault safety errors during the task than did control subjects. Roadside target identification performance and safety errors were predicted by scores on standardized tests of visual, cognitive, and motor function. Drivers with stroke are impaired in a task of visual search and recognition of roadside targets whose demands on visual perception, attention, executive functions, and memory probably increased the cognitive load and worsened their driving safety.

The Impact of Road Familiarity on the Perception of Traffic Signs – Eye Tracking Case Study

2017 ◽  
Author(s):  
Dario Babic ◽  
Darko Babic ◽  
Andelko Šcukanec
Keyword(s):  
Eye Tracking ◽  
Visual Information ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Driving Experience ◽  
The Road ◽  
Road Users ◽  
On The Road ◽  
The Impact

Traffic sign visual information provides road users with the basic instructions regarding route selection, safety at intersections, warnings on physical obstacles on the road and safe route marking. The use of sophisticated eye tracking systems is an efficient way to analyse the influence of traffic signs on drivers’ behaviour. In this paper, the drivers’ perception of traffics signs has been analysed using such a system. The aim of this paper is to determine how the perception of traffic signs changes according to the frequency of driving on a specific route or according to the route familiarity. The results show that the drivers’ perception of traffic signs declines as they get familiar with the route and road conditions. In addition, older drivers having more driving experience perceive fewer signs and elements from the environment because they are often led by their own experience and knowledge, so they do not need the same amount of information as compared to younger drivers.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

2021 ◽  
Vol 11 (8) ◽  
pp. 3666
Author(s):  
Zoltán Fazekas ◽  
László Gerencsér ◽  
Péter Gáspár
Keyword(s):  
Change Detection ◽  
Traffic Safety ◽  
Recognition System ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safety Risk ◽  
Urban Road ◽  
Environment Type ◽  
Cusum Method

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

Visual Recognition System of Elastic Rail Clips for Mass Rapid Transit Systems

2007 ◽  
Author(s):  
Hsiang-Yu Hsieh ◽  
Nanming Chen ◽  
Ching-Lung Liao
Keyword(s):  
Visual Recognition ◽  
Experimental Testing ◽  
Recognition System ◽  
Morphological Processing ◽  
Driving Safety ◽  
Rapid Transit ◽  
Grey Level ◽  
Railway Transportation ◽  
Transit Systems ◽  
Mass Rapid Transit

In recent years, the railway transportation system has become one of the main means of transportation. Therefore, driving safety is of great importance. However, because of the potential of multiple breaks of elastic rail clips in a fixed rail, accidents may occur when a train passes through the track. This paper presents the development of a computer visual recognition system which can detect the status of elastic rail clips. This visual recognition system can be used in mass rapid transit systems to reduce the substantial need of manpower for checking elastic rail clips at present. The visual recognition system under current development includes five components: preprocessing, identification of rail position, search of elastic rail clip regions, selection of the elastic rail clip, and recognition of the elastic rail clip. The preprocessing system transforms the colored images into grey-level images and eliminates noises. The identification of rail position system uses characteristics of the grey-level variation and confirms the rail position. The search system uses wavelet transformation to carry out the search of elastic rail clip regions. The selection system finds a suitable threshold, using techniques from morphological processing, object search and image processing. The recognition system processes characteristics and structures of elastic rail clips. Experimental testing shows the ability of the developed system to recognize both normal elastic rail clip images and broken elastic rail clip images. This result confirms the feasibility in developing such a visual recognition system.

scholarly journals Forest fire danger rating in complex topography – results from a case study in the Bavarian Alps in autumn 2011

2013 ◽  
Vol 13 (9) ◽  
pp. 2157-2167 ◽  
Author(s):  
C. Schunk ◽  
C. Wastl ◽  
M. Leuchner ◽  
C. Schuster ◽  
A. Menzel
Keyword(s):  
Forest Fire ◽  
Temperature Inversion ◽  
Fire Danger ◽  
Complex Topography ◽  
Pressure System ◽  
Drought Period ◽  
Bavarian Alps ◽  
Forest Fire Danger ◽  
Fire Danger Indices

Abstract. Forest fire danger rating based on sparse meteorological stations is known to be potentially misleading when assigned to larger areas of complex topography. This case study examines several fire danger indices based on data from two meteorological stations at different elevations during a major drought period. This drought was caused by a persistent high pressure system, inducing a pronounced temperature inversion and its associated thermal belt with much warmer, dryer conditions in intermediate elevations. Thus, a massive drying of fuels, leading to higher fire danger levels, and multiple fire occurrences at mid-slope positions were contrasted by moderate fire danger especially in the valleys. The ability of fire danger indices to resolve this situation was studied based on a comparison with the actual fire danger as determined from expert observations, fire occurrences and fuel moisture measurements. The results revealed that, during temperature inversion, differences in daily cycles of meteorological parameters influence fire danger and that these are not resolved by standard meteorological stations and fire danger indices (calculated on a once-a-day basis). Additional stations in higher locations or high-resolution meteorological models combined with fire danger indices accepting at least hourly input data may allow reasonable fire danger calculations under these circumstances.

scholarly journals Cellular Automata as an Example for Advanced Beginners’ Level Coding Exercises in a MOOC on Test Driven Development

2017 ◽  
Vol 7 (2) ◽  
pp. 125 ◽  
Author(s):  
Thomas Staubitz ◽  
Ralf Teusner ◽  
Christoph Meinel ◽  
Nishanth Prakash
Keyword(s):  
Cellular Automata ◽  
Crucial Role ◽  
Computer Programming ◽  
Good Practice ◽  
Experiential Knowledge ◽  
Suitable Candidate ◽  
Test Driven Development ◽  
Automated Evaluation ◽  
Programming Skills

Programming tasks are an important part of teaching computer programming as they foster students to develop essential programming skills and techniques through practice.  The design of educational problems plays a crucial role in the extent to which the experiential knowledge is imparted to the learner both in terms of quality and quantity. Badly designed tasks have been known to put-off students from practicing programming. Hence, there is a need for carefully designed problems. Cellular Automata programming lends itself as a very suitable candidate among problems designed for programming practice. In this paper, we describe how various types of problems can be designed using concepts from Cellular Automata and discuss the features which make them good practice problems with regard to instructional pedagogy. We also present a case study on a Cellular Automata programming exercise used in a MOOC on Test Driven Development using JUnit, and discuss the automated evaluation of code submissions and the feedback about the reception of this exercise by participants in this course. Finally, we suggest two ideas to facilitate an easier approach of creating such programming exercises.

scholarly journals Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

2019 ◽  
Vol 11 (12) ◽  
pp. 1453 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Lili Lin ◽  
Chenglu Wen ◽  
Chenhui Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Laser Scanning ◽  
Large Scale ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Point Clouds ◽  
Traffic Sign ◽  
Traffic Signs ◽  
3D Point Clouds ◽  
Traffic Environment

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

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