scholarly journals An Enhanced Artificial Intelligence-Based Approach Applied to Vehicular Traffic Signs Detection and Road Safety Enhancement

2021 ◽  
Vol 6 (1) ◽  
pp. 672-683
Author(s):  
Anass Barodi ◽  
Abderrahim Bajit ◽  
Taoufiq El Harrouti ◽  
Ahmed Tamtaoui ◽  
Mohammed Benbrahim
Keyword(s):  
Artificial Intelligence ◽  
Road Safety ◽  
Vehicular Traffic ◽  
Traffic Signs ◽  
Safety Enhancement

Artificial intelligence based ensemble model for prediction of vehicular traffic noise

2020 ◽  
Vol 180 ◽  
pp. 108852 ◽  
Author(s):  
Vahid Nourani ◽  
Hüseyin Gökçekuş ◽  
Ibrahim Khalil Umar
Keyword(s):  
Artificial Intelligence ◽  
Traffic Noise ◽  
Ensemble Model ◽  
Vehicular Traffic

DETEKSI DAN REKOGNISI RAMBU-RAMBU LALU LINTAS DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Kurniawan Nur Ramadhani ◽  
M.Syahrul Mubarok ◽  
Agnes Dirgahayu Palit
Keyword(s):  
Support Vector Machines ◽  
Road Safety ◽  
Support Vector ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Histogram Of Oriented Gradient ◽  
Vector Machines ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu 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 Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score.Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide 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 Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) 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 Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

Automated Real-Time Roadway Asset Inventory using Artificial Intelligence

2020 ◽  
Vol 2674 (11) ◽  
pp. 220-234
Author(s):  
Nima Kargah-Ostadi ◽  
Ammar Waqar ◽  
Adil Hanif
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Asset Management ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Training Dataset ◽  
Traffic Signs ◽  
Asset Inventory

Roadway asset inventory data are essential in making data-driven asset management decisions. Despite significant advances in automated data processing, the current state of the practice is semi-automated. This paper demonstrates integration of the state-of-the-art artificial intelligence technologies within a practical framework for automated real-time identification of traffic signs from roadway images. The framework deploys one of the very latest machine learning algorithms on a cutting-edge plug-and-play device for superior effectiveness, efficiency, and reliability. The proposed platform provides an offline system onboard the survey vehicle, that runs a lightweight and speedy deep neural network on each collected roadway image and identifies traffic signs in real-time. Integration of these advanced technologies minimizes the need for subjective and time-consuming human interventions, thereby enhancing the repeatability and cost-effectiveness of the asset inventory process. The proposed framework is demonstrated using a real-world image dataset. Appropriate pre-processing techniques were employed to alleviate limitations in the training dataset. A deep learning algorithm was trained for detection, classification, and localization of traffic signs from roadway imagery. The success metrics based on this demonstration indicate that the algorithm was effective in identifying traffic signs with high accuracy on a test dataset that was not used for model development. Additionally, the algorithm exhibited this high accuracy consistently among the different considered sign categories. Moreover, the algorithm was repeatable among multiple runs and reproducible across different locations. Above all, the real-time processing capability of the proposed solution reduces the time between data collection and delivery, which enhances the data-driven decision-making process.

A Survey on Awareness of Traffic Signs among Youth in Qatar

2014 ◽  
Vol 587-589 ◽  
pp. 2133-2136
Author(s):  
Khaled Shaaban
Keyword(s):  
Road Safety ◽  
Strong Relationship ◽  
The United States ◽  
Age Group ◽  
Road Accidents ◽  
Traffic Signs ◽  
Significance Level ◽  
Driving Experience ◽  
Young Drivers ◽  
Minor Injuries

In Qatar, road accidents kill approximately 200 people every year and injure or disable many others. In addition to 205 fatalities in 2010, road accidents resulted in 586 major injuries and 4,723 minor injuries in Qatar. Road accidents are responsible for approximately 18% of the total deaths in Qatar compared to approximately 2% in the United States. Young drivers are known to be a problem age group for road safety in several countries. The objective of this paper is to understand the young driver's knowledge of the traffic signs in Qatar and to determine the extent to which drivers are different in their knowledge when it comes to gender, age, and nationality. The results showed a high percentage of the young drivers were familiar with traffic signs. The results found no significant relationship between the success rate and the gender of the respondents, their nationality, and the driving experience at the 10% significance level. However, there was a strong relationship between the response and age of the respondents.

scholarly journals Autonomous Recognition Model

2021 ◽  
Vol 10 (6) ◽  
pp. 67-73
Author(s):  
Akash gupta ◽  
Rahat Ali ◽  
Abhay Pratap Singh ◽  
P.Raja Kumar
Keyword(s):  
Artificial Intelligence ◽  
Automobile Industry ◽  
Autonomous Vehicles ◽  
Future Generation ◽  
Autonomous Driving ◽  
Traffic Signs ◽  
Use Of Technology ◽  
Human Driver ◽  
New Research ◽  
Self Driving Cars

Nowdays we are witnessing the technology transforming everything the way we used to do things and how the automobile industry is transforming itself with the use of technology IOT,Artificial intelligence,Machine learning.Companies shifting its products and its utilities in diferent way and they now want to acquire and introduce level-5 autonomous to future generation and big automobile companies are trying to achieve autonomous vechicles and we have researhed about the model that will help in assisting autonomous vechicles and trying to achieve that.We will develop this model with help of technologies like Artificial intelligence,Machine learning,Deep learning.Autonomous vehcicles will become a reality on our roads in the near future. However, the absence of a human driver requires technical solutions for a range of issues, and these are still being developed and optimised. It is a great contribution for the automotive industry which is going towards innovation and economic growth. If we talking about some past decade the momentum of new research and the world is now at the very advanced stage of technological revolution. “Autonomous-driving” vehicles. The term Self-driving cars, autonomous car, or the driverless cars have different name with common objective. The main focus is to keep the human being out of the vehicle control loop and to relieve them from the task of driving. Everyday automotive technology researchers solve challenges. In the future, without human assistance, robots will produce autonomous vehicles using IoT technology based on customer needs and prefer that these vehicles are more secure and comfortable in mobility systems such as the movement of people or goods. We will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles .This model we have tested it and resulted in 95% accuracy.

scholarly journals Analisis Daerah Rawan Kecelakaan (Black Site) Dan Titik Rawan Kecelakaan (Black Spot) Provinsi Lampung

2021 ◽  
Vol 5 (1) ◽  
pp. 40-51
Author(s):  
Yogi Oktopianto ◽  
Siti Shofiah ◽  
Faishal Andhi Rokhman ◽  
Kanthi Pangestu Wijayanthi ◽  
Eka Krisdayanti
Keyword(s):  
Land Use ◽  
Road Safety ◽  
Traffic Accidents ◽  
Black Spot ◽  
Z Score ◽  
Traffic Signs ◽  
Main Indicator ◽  
Prone Area ◽  
Minor Injuries

Traffic accidents are the main indicator of road safety levels. There were 2,225 traffic accidents throughout 2019 in Lampung Province with 724 deaths, 1240 seriously injured, and 2015 minor injuries. The research was conducted to the identification of the black site and black spot in Lampung Province. The method used in this research is the EAN, Z-score, Frequency of accidents method calculates accident-prone areas to get the value of the accident location (Black Site) and the Cumulative Summary method to identify the black spot. The results showed from 93 roads, there is 1 road which is the highest black link of each road status. The accident-prone area (black site) is Jalan Lintas Tengah Sumatra and accident-prone points (black spot) at KM 18-26 are influenced by land use, road geometric, and traffic signs.

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