scholarly journals A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

2021 ◽  
Vol 11 (17) ◽  
pp. 8210
Author(s):  
Chaeyoung Lee ◽  
Hyomin Kim ◽  
Sejong Oh ◽  
Illchul Doo
Keyword(s):  
Deep Learning ◽  
Traffic Safety ◽  
Research Direction ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Video Data ◽  
Future Research ◽  
The Road ◽  
Detection Model ◽  
On The Road

This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using FFmpeg for model production, car collision types are classified, and only the head-on collision types are processed by using the deep learning object-detection algorithm YOLO (You Only Look Once). Using the car accident detection model that we built and the provided road obstacle-detection model, we programmed, for when the model detects abnormalities on the road, warning notification and photos that captures the accidents or obstacles, which are then transferred to the application. The proposed service was verified through application notification simulations and virtual experiments using CCTVs in Daegu, Busan, and Gwangju. By providing services, the goal is to improve traffic safety and achieve the development of a self-driving vehicle sector. As a future research direction, it is suggested that an efficient CCTV control system be introduced for the transportation environment.

scholarly journals Obstacle Detection and Safely Navigate the Autonomous Vehicle from Unexpected Obstacles on the Driving Lane

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4719
Author(s):  
Malik Haris ◽  
Jin Hou
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicle ◽  
Research Area ◽  
Visual Navigation ◽  
Obstacle Detection ◽  
The Road ◽  
Markov Random ◽  
Active Research ◽  
On The Road ◽  
Active Research Area

Nowadays, autonomous vehicle is an active research area, especially after the emergence of machine vision tasks with deep learning. In such a visual navigation system for autonomous vehicle, the controller captures images and predicts information so that the autonomous vehicle can safely navigate. In this paper, we first introduced small and medium-sized obstacles that were intentionally or unintentionally left on the road, which can pose hazards for both autonomous and human driving situations. Then, we discuss Markov random field (MRF) model by fusing three potentials (gradient potential, curvature prior potential, and depth variance potential) to segment the obstacles and non-obstacles into the hazardous environment. Since the segment of obstacles is done by MRF model, we can predict the information to safely navigate the autonomous vehicle form hazardous environment on the roadway by DNN model. We found that our proposed method can segment the obstacles accuracy from the blended background road and improve the navigation skills of the autonomous vehicle.

scholarly journals Study on Microscopic Simulation Technology of Operation Process of Unmanned Vehicle Based on Virtual Track

2020 ◽  
Vol 198 ◽  
pp. 03020
Author(s):  
Shiyao Teng ◽  
Jinjin Tang
Keyword(s):  
Research Direction ◽  
Unmanned Vehicles ◽  
Future Research ◽  
Microscopic Simulation ◽  
Cellular Automata Model ◽  
The Road ◽  
Operation Process ◽  
Strong Controllability ◽  
On The Road ◽  
The Stability

With the continuous development of unmanned driving technology, more and more unmanned vehicles are on the road to start testing, under this circumstance, how to control these unmanned vehicles has become an urgent issue. Based on the basic idea of virtual track and cellular automata model, this paper researches and designs a microscopic simulation system of the operation process of unmanned vehicles. The aim of this paper is to use the idea of strong controllability of railways to control unmanned vehicles in the form of train diagram to ensure their orderly safety on the road. The stability and correctness of the system are verified through case analysis. At the end of the article, the future research direction is prospected.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

scholarly journals The Effects of ITS Application in Speed Management on State Road From Mali Pozarevac to Kragujevac

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Tomislav Petrović ◽  
Miloš Milosavljević ◽  
Milan Božović ◽  
Danislav Drašković ◽  
Milija Radović
Keyword(s):  
Traffic Safety ◽  
Traffic Management ◽  
Quality Data ◽  
Transport Systems ◽  
Speed Limit ◽  
Accident Risk ◽  
The Road ◽  
Road Users ◽  
Speed Management ◽  
On The Road

The application of intelligent transport systems (hereinafter ITSs) on roads enables continuous monitoring of road users during a whole year with the aim to collect good-quality data based on which the more complex analyses could be done, such as monitoring of certain traffic safety indicators. Automatic traffic counters are one of the most commonly implemented ITSs for collecting traffic flow parameters that are relevant for traffic management on state roads in Republic of Serbia. This paper presents one of the possible ways to collect, analyze and present data on road users’ speeds using automatic traffic counters, where certain traffic safety indicators are analyzed in terms of road users’ compliance with the speed limit on the road section from Mali Pozarevac to Kragujevac. Based on the analyses of data downloaded from automatic traffic counters, it is observed that an extremely high percentage of vehicles drive at speed higher than the speed limit, indicating clearly to higher traffic accident risk, as well as to the need for a tendency to implement speed management on roads using ITS in the forthcoming period.

scholarly journals Exploring the Informal Communication of Driver-to-Driver on Roads: A Case Study of Durban City, South Africa.

2021 ◽  
Vol 67 (4) ◽  
pp. 1-8
Author(s):  
Jacob Adedayo Adedeji ◽  
Xoliswa Feikie
Keyword(s):  
South African ◽  
Traffic Safety ◽  
Road Traffic ◽  
Structured Interview ◽  
Road User ◽  
Novice Drivers ◽  
The Road ◽  
Road Users ◽  
Hand Signal ◽  
On The Road

Road traffic fatality is rated as one of the ten causes of death in the world and with various preventive measures on a global level, this prediction is only placed on flat terrain and didn’t reduce. Nevertheless, road users’ communication is an essential key to traffic safety. This communication, be it formal or informal between the road users is an important factor for smooth traffic flow and safety. Communication language on roads can be categorized into; formal device-based signal (formal signal), formal hand signal (formal signal), informal device-based signal (informal signal), and informal gesture-based signal (everyday signal). However, if the intent of the message conveys is not properly understood by the other road user, mistakes and errors may set in. Overall, the formal signal is based on explicit learning which occurs during the driving training and the license testing process and the informal, implicit learning occur during the actual driving process on the road unintentionally. Furthermore, since the informal signal is not a prerequisite to driving or taught in driving schools, novice drivers are clueless and thus, might have contributed to errors and mistakes which leads to traffic fatalities. Therefore, this study seeks to document the informal means of communication between drivers on South African roads. Consequently, a qualitative semi-structured interview questionnaire would be used in the collection of informal signals, which were predominantly used on South African roads from driving instructors and thereafter, a focus group of passengers’ car, commercial and truck drivers will be used to validate the availability and their understanding of these informal signals using a Likert-type scale for the confidence level. In conclusion, the information gathered from this study will help improve road safety and understanding of road users especially drivers on the necessity of communication and possible adaptation for other developing countries.

scholarly journals STUDI KERUSAKAN JALAN DITINJAU DARI FAKTOR SETEMPAT (STUDI KASUS RUAS JALAN BLANGKEJEREN – LAWE AUNAN)

2018 ◽  
Vol 1 (3) ◽  
pp. 667-678
Author(s):  
Mulyadi Mulyadi ◽  
Muhammad Isya ◽  
Sofyan M. Saleh
Keyword(s):  
Traffic Safety ◽  
Condition Index ◽  
Road Surface ◽  
Primary Data ◽  
Soil Conditions ◽  
Pavement Condition ◽  
Local Factors ◽  
The Road ◽  
Pavement Condition Index ◽  
On The Road

Abstract: Blangkejeren - Lawe Aunan road conditions overall is on the slopes of the mountains which is strongly influenced by local environmental factors such as drainage, topography, soil conditions, material conditions and vehicle load conditions across the road. It should be noted in order to avoid a decrease in the road quality due to road surface damage that can affect the traffic safety, comfort and smoothness.. Therefore, it is necessary to study the evaluation of the condition of the damaged road surface and the local factors that affect the damage in order to avoid a decrease in the roads quality. This study took place on Blangkejeren - Lawe Aunan roads started from Sta. 529 + 700 - Sta. 535 + 206. Generally, the condition of roads in this segment were found damage that disturb the comfort, smoothness and safety of the roads users. In this study, the primary data obtained by actual surveys in the form of data field length, width, area, and depth of each type of damage as well as local factors that lead to such damage. Actual field surveys conducted along the 5.506 km, with the distance interval of each segment is 100 m. The secondary data obtained from the relevant institutions and other materials related to this research. This study analyzed the PCI method (Pavement Condition Index) to obtain the level of damage in order to know how to handle, while for the identification of the damage done by observation factors descriptively appropriate observation in the field such as the number of damage points. The results of this study found that the type of damage caused to roads is damage to the cover layer, a hole, and curly. This type of damage that commonly occurs on the road Blangkejeren - Lawe Aunan is damage to the edges with a percentage of 87.30%. The local factors that greatly affect drainage on the percentage of damage is 62.00%. PCI average value is 13.47 which indicates a very bad condition (very poor) and requires maintenance or improvement of reconstruction.Abstrak: Kondisi jalan Blangkejeren – Lawe Aunan secara keseluruhan berada di lereng pegunungan sangat dipengaruhi oleh faktor lingkungan setempat seperti drainase, topografi, kondisi tanah, kondisi material dan kondisi beban kendaraan yang melintasi jalan tersebut. Hal ini perlu diperhatikan agar tidak terjadi penurunan kualitas jalan akibat kerusakan permukaan jalan sehingga dapat mempengaruhi keamanan, kenyamanan, dan kelancaran dalam berlalu lintas. Oleh karena itu, perlu dilakukan penelitian evaluasi terhadap kondisi permukaan jalan yang mengalami kerusakan serta faktor setempat yang mempengaruhi kerusakan tersebut agar tidak terjadi penurunan kualitas jalan. Penelitian ini mengambil lokasi di ruas jalan Blangkejeren – Lawe Aunan yang dimulai dari Sta. 529+700 - Sta. 535+206. Umumnya kondisi ruas jalan pada segmen ini banyak ditemukan kerusakan-kerusakan yang dapat mengganggu kenyamanan, kelancaran, dan keamanan pengguna jalan. Dalam penelitian ini data primer diperoleh dengan melakukan survei aktual lapangan yaitu berupa data panjang, lebar, luasan, dan kedalaman tiap jenis kerusakan serta faktor setempat yang mengakibatkan kerusakan tersebut. Survei aktual lapangan dilakukan sepanjang 5,506 km, dengan jarak interval setiap segmen adalah 100 m. Adapun data sekunder diperoleh dari lembaga terkait dan bahan lainnya yang berhubungan dengan penelitian ini. Penelitian ini dianalisis dengan metode PCI (Pavement Condition Index) untuk mendapatkan tingkat kerusakan agar diketahui cara penanganannya, sedangkan untuk identifikasi faktor kerusakannya dilakukan dengan pengamatan secara diskriptif sesuai hasil pengamatan di lapangan berupa jumlah titik kerusakan. Hasil penelitian ini didapatkan bahwa jenis kerusakan yang terjadi pada ruas jalan adalah kerusakan lapisan penutup, lubang, dan keriting. Jenis kerusakan yang umum terjadi pada ruas jalan Blangkejeren – Lawe Aunan adalah kerusakan tepi dengan persentase 87,30 %. Faktor setempat yang sangat mempengaruhi kerusakan adalah drainase dengan persentase 62,00%. Nilai PCI rata-rata yaitu 13,47 yang menunjukkan kondisi sangat buruk (very poor) dan memerlukan pemeliharaan peningkatan atau rekonstruksi.

PREVENTION OF ADMINISTRATIVE OFFENSES IN THE FIELD OF ROAD TRAFFIC BY THE INTERNAL AFFAIRS BODIES

2021 ◽  
pp. 38-40
Author(s):  
А.Р. Исмагилова
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
The State ◽  
Road Accidents ◽  
The Road ◽  
Internal Affairs ◽  
On The Road

В статье раскрываются полномочия сотрудников подразделений пропаганды Государственной инспекции безопасности дорожного движения в целях профилактики дорожно-транспортных происшествий и травматизма на дороге. The article reveals the powers of the employees of the propaganda units of the State Traffic Safety Inspectorate in order to prevent road accidents and injuries on the road.

On the Road to Ephesus

2015 ◽  
pp. 102-125
Author(s):  
Suzanne Roff-Wexler
Keyword(s):  
Big Data ◽  
Future Research ◽  
Review Of Literature ◽  
Healthcare Organizations ◽  
Research Directions ◽  
The Road ◽  
Future Research Directions ◽  
On The Road ◽  
Definition Of

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.

Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR

2019 ◽  
Vol 2673 (9) ◽  
pp. 62-71 ◽  
Author(s):  
Zhenyao Zhang ◽  
Jianying Zheng ◽  
Hao Xu ◽  
Xiang Wang
Keyword(s):  
High Resolution ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Vehicle Detection ◽  
Traffic Data ◽  
The Road ◽  
Detection And Tracking ◽  
Area Of Interest ◽  
Vehicle Detection And Tracking ◽  
On The Road

The problem of traffic safety has become increasingly prominent owing to the increase in the number of cars. Traffic accidents often occur in an instant, which makes it necessary to obtain traffic data with high resolution. High-resolution micro traffic data (HRMTD) indicates that the spatial resolution reaches the centimeter level and that the temporal resolution reaches the millisecond level. The position, direction, speed, and acceleration of objects on the road can be extracted with HRMTD. In this paper, a LiDAR sensor was installed at the roadside for data collection. An adjacent-frame fusion method for vehicle detection and tracking in complex traffic circumstances is presented. Compared with the previous research, objects can be detected and tracked without object model extraction or a bounding box description. In addition, problems caused by occlusion can be improved using adjacent frames fusion in the vehicle detection and tracking algorithms in this paper. The data processing procedure are as follows: selection of area of interest, ground point removal, vehicle clustering, and vehicle tracking. The algorithm has been tested at different sites (in Reno and Suzhou), and the results demonstrate that the algorithm can perform well in both simple and complex application scenarios.

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