Rapid Comprehension of Verbal and Symbolic Traffic Sign Messages

1979 ◽  
Vol 21 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Jerry G. Ells ◽  
Robert E. Dewar
Keyword(s):  
Reaction Time ◽  
Motor Vehicle ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Verbal Messages ◽  
The Times ◽  
Practical Implications

A “same”-“different” reaction time procedure was used in two experiments to measure the times required to comprehend the meanings of projected slides of traffic signs. The results indicated that signs with symbolic messages could be understood more quickly than those with verbal messages. Visually degrading the signs resulted in a greater decrement in performance for verbal than for symbolic signs. Correlational analyses demonstrated reaction time to correlate significantly with a previously obtained measure of sign legibility taken from a moving motor vehicle on a roadway. The utility of reaction time as an index of traffic sign adequacy is discussed along with some possible practical implications of the research.

scholarly journals PERILAKU BERLALU LINTAS YANG MENDUKUNG KESELAMATAN DI JALAN RAYA

2017 ◽  
Vol 14 (1) ◽  
pp. 10
Author(s):  
Eliza Purnamasari Poei ◽  
J.Dwijoko Ansusanto
Keyword(s):  
Motor Vehicle ◽  
Traffic Sign ◽  
School Work ◽  
Point Of Interest ◽  
Road Users ◽  
Vehicle Technology ◽  
Catch Up ◽  
The Times ◽  
The Moment ◽  
Special Region

Yogyakarta as student city and tourist destination that will cause the number of immigrantsstudying or sightseeing in Special Region of Yogyakarta increasing from year to year. Yogyakartais like a miniature Indonesia, a wide variety of people with cultures and tribes can befound here, they perform daily activities with their behavior and habits of each. The number ofmotorvehicles in Yogyakarta also increased from year to year. Motor vehicle technology is moreadvanced offset by road users catch up with the times of fast-paced, causing traffic on the highwayis getting crowded. If road users is not an orderly way, behaving arbitrarily can endanger otherroad users. The purpose of this study are : a) To investigate the behavior of road users, both driversand pedestrians in DIY, b) analyzing public opinion on the behavior of road users when passtrafficon the highway. Direct observation in the field and deployment questionnaire conducted inDIY. The analysis showed that most motorcyclists do not obey traffic sign or jumping red. Overtakethe vehicle from the left side of the overtaken vehicle. Turn right or left is not lit the lampsign. That respondents believe that the implementation of orderly traffic both closely associatedwith “The application of sanctions/penalties consequently” (64%). Accident ever experienced 54%of respondents in the province is a collision with a motorcycle. The main cause of accidents due tolack of concentration, 33% of accidents occurred at noon. According to respondents the reasondoes not obey traffic signs / road markings 65% because “ there are no policemen watching”.Usually respondents APILL light violation 61% at the moment “in a hurry because nearly/toolate to school/work/point of interest. Abstrak : Yogyakarta sebagai kota pelajar dan daerah tujuan Wisata menyebabkan Jumlah pendatangyang akan menuntut ilmu maupun berwisata di Daerah Istimewa Yogyakarta semakinmeningkat dari tahun ke tahun, Yogyakarta bagaikan miniaturnya Indonesia, berbagai ragam orangdengan budaya dan suku dapat dijumpai disini, mereka melakukan aktifitas sehari-hari dengan perilakudan kebiasaan mereka masing-masing. Jumlah kendaraan bermotor yang ada di DIY jugameningkat dari tahun ke tahun. Teknologi kendaraan bermotor yang semakin maju diimbangi olehpengguna jalan yang mengikuti derap langkah perkembangan zaman yang serba cepat, menyebabkanlalu-lintas di jalan raya semakin padat. Jika pengguna jalan tidak tertib, berperilaku semaunyasendiri dapat membahayakan keselamatan pengguna jalan yang lain.. Tujuan penelitian iniadalah : a) Menginvestigasi perilaku pengguna jalan , baik pengemudi maupun penyeberang jalandi DIY, b) Menganalisis penilaian masyarakat terhadap perilaku pengguna jalan saat berlalu-lintasdi jalan raya. Pengamatan langsung di lapangan dan penyebaran questioner dilakukan di wilayahDIY.Hasil analisis menunjukkan bahwa sebagian besar pengguna sepeda motor tidak mentaatirambu maupun APILL, menyalip kendaraan dari sisi kiri kendaraan yang disalip, berbelok kananatau kiri tidak memberi tanda lampu sign . Pendapat responden bahwa pelaksanaan tertib lalulintasyang baik berkaitan erat dengan “Penerapan sanksi/hukuman secara konsekuen”(64%). Kecelakaanyang pernah dialami responden di DIY 54% adalah tabrakan dengan sepeda motor, sebabutama kecelakaan karena kurang konsentrasi (38%), kecelakaan terjadi 33% pada siang hari..Menurut responden alasan tidak mematuhi rambu lalu-lintas /marka jalan 65% karena “tidak adapolisi yang mengawasi”. Biasanya responden melanggar lampu APILL pada saat “Tergesa-gesakarena hampir /sudah terlambat ke sekolah/tempat kerja/tempat tujuan” (61%).Kata kunci:: lalu-lintas, perilaku, keselamatan, pengguna jalan, kendaraan

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.

The Semantic Differential as an Index of Traffic Sign Perception and Comprehension

1977 ◽  
Vol 19 (2) ◽  
pp. 183-189 ◽  
Author(s):  
Robert E. Dewar ◽  
Jerry G. Ells
Keyword(s):  
Semantic Differential ◽  
The Other ◽  
Traffic Sign ◽  
Two Factors ◽  
Valid Instrument ◽  
Paper And Pencil ◽  
Verbal Messages ◽  
Highly Correlated

There is a need to develop and validate simple, inexpensive techniques for the evaluation of traffic sign messages. This paper examines the semantic differential (a paper-and-pencil test which measures psychological meaning) as a potential instrument for such evaluation. Two experiments are described, one relating semantic differential scores to comprehension and the other relating this index to glance legibility. The data indicate that semantic differential scores on all four factors (evaluative, activity, potency, and understandability) were highly correlated with comprehension of symbolic messages. These scores were unrelated to glance legibility of verbal messages, but two factors (evaluative and understandability) did correlate with glance legibility of symbolic messages. It was concluded that the semantic differential is a valid instrument for evaluating comprehension of symbolic sign messages and that it has advantages over other techniques.

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.

scholarly journals Traffic Sign Classification and Detection of Indian Traffic Signs using Deep Learning

2021 ◽  
pp. 215-219
Author(s):  
Manjiri Bichkar ◽  
Suyasha Bobhate ◽  
Prof. Sonal Chaudhari
Keyword(s):  
Classification Model ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Electric Cars ◽  
Blob Detection ◽  
Sign Recognition ◽  
Testing Dataset ◽  
Self Driving Cars ◽  
Traffic Sign Classification

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.

scholarly journals Automatic mapping of traffic signs

2021 ◽  
Vol 4 ◽  
pp. 1-6
Author(s):  
Bence Dusek ◽  
Mátyás Gede
Keyword(s):  
Inertial Measurement Unit ◽  
Local Coordinate System ◽  
Point Of View ◽  
Measurement Unit ◽  
Absolute Position ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Automatic Mapping ◽  
The Government ◽  
Cost Efficient

Abstract. Nowadays, people easily can get into their cars and drive hundreds of kilometers in a few hours, but for that to work efficiently a system of rules must be applied and those rules have to be communicated transparently. This is why traffic signs are an influential part of our lives and every kind of information about each is helping the government, the community, and the drivers. This paper presents a novel and cost-efficient method for acquiring information on traffic signs, such like the category and the 3D position. The former can be gained using camera images and a Convolutional Neural Network model. The latter can be obtained using positioning devices.With the help of a GNSS device the absolute position of the vehicle can be learned and based on that a local coordinate system can be established. From the vehicle’s point of view the coordinates and the orientation of the traffic sign can be acquired by applying a stereo camera and an IMU (Inertial Measurement Unit) sensor. Then, with the help of these attributes a large database can be built, maintained, and updated. This project displays that adequately precise data can easily be accessible using a few cheap devices and sensors.

Traffic-Signs Recognition System Based on FCM and Content-Based Image Retrieval

2012 ◽  
pp. 60-72
Author(s):  
Yue Li ◽  
Wei Wang
Keyword(s):  
Image Retrieval ◽  
Recognition System ◽  
Emerging Technology ◽  
Content Based Image Retrieval ◽  
Segmentation Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Artificial Intelligent ◽  
Traffic System ◽  
Two Phases

Artificial intelligent (AI) driving is an emerging technology, freeing the driver from driving. Some techniques for automatically driving have been developed; however, most can only recognize the traffic signs in particular groups, such as triangle signs for warning, circle signs for prohibition, and so forth, but cannot tell the exact meaning of every sign. In this paper, a framework for a traffic system recognition system is proposed. This system consists of two phases. The segmentation method, fuzzy c-means (FCM), is used to detect the traffic sign, whereas the Content-Based Image Retrieval (CBIR) method is used to match traffic signs to those in a database to find the exact meaning of every detected sign.

scholarly journals Dense-RefineDet for Traffic Sign Detection and Classification

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6570
Author(s):  
Chang Sun ◽  
Yibo Ai ◽  
Sheng Wang ◽  
Weidong Zhang
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Design Method ◽  
Contextual Information ◽  
Real Life ◽  
Single Shot ◽  
Traffic Sign ◽  
Traffic Signs ◽  
High Level ◽  
Anchor Design

Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (RefineDet) to maintain a suitable accuracy–speed trade-off. We constructed a dense connection-related transfer-connection block to combine high-level feature layers with low-level feature layers to optimize the use of the higher layers to obtain additional contextual information. Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs (recall: 84.3%, 95.2%, and 92.6%; precision: 83.9%, 95.6%, and 94.0%). Moreover, experiments using the Caltech pedestrian dataset indicated that the miss rate of Dense-RefineDet was 54.03% (pedestrian height > 20 pixels), which outperformed other state-of-the-art methods.

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