Prevent Road Accidents Research Articles

ИНОСТРАННЫЙ И ОТЕЧЕСТВЕННЫЙ ОПЫТ ПРОФИЛАКТИКИ ДОРОЖНО-ТРАНСПОРТНЫХ ПРОИСШЕСТВИЙ С ИСПОЛЬЗОВАНИЕМ СИСТЕМ АВТОМАТИЧЕСКОЙ ФИКСАЦИИ ПРАВОНАРУШЕНИЙ

Ensuring road safety is a complex and multifaceted problem. Therefore, measures to prevent road accidents (hereinafter referred to as road accidents) should be comprehensive. Accident prevention activities directly affect their dynamics. Since the line between them lies in the sphere of consequences, and violations can be and often are completely identical, preventive work aimed at preventing road accidents can be generally qualitatively homogeneous. Organizational support of preventive measures is the content of road accident prevention and it is in their implementation that the specific features of this type of prevention as an independent subsystem of social prevention are revealed. The core of the prevention system is the development and implementation of a set of preventive measures. Prevention of offenses is determined by the general goal of social prevention — to prevent offenses, to prevent antisocial behavior, and is carried out at the same levels, in the same forms and by the same means, taking into account the delicacy. In this area, it is impossible to do without the achievements of technological progress. In all EU member states, without exception, technical means of ensuring road safety have become widely used.

Features of administrative and legal prevention of road and transport accidents under the conditions of marital state

Features of administrative and legal prevention of road and transport accidents under the conditions of marital state

Analysis of 5-year monitoring of road traffic injuries rates in Kazan

The objective of the study is to establish a set of possible causes and patterns that influence the frequency and severity of road traffic accidents with fatalities. The results of the 5-year monitoring analysis of fatal road traffic injuries in Kazan are presented. We analyzed 662 cases. It was established that the main risk group for fatal road accidents includes subjects aged 20-39. The risk group for drivers is 30-39 years old, passengers 20-29 years old, and pedestrians 55-64 years old. Children account for 3% of all traffic fatalities; 80% of them are under 10 years old. Most children (70%) were in the vehicle at the time of the crash, and their parents may not have been using special equipment (car seats and seat belts). We identified the most traumatic months of the year, days of the week and times of the day for drivers, passengers and pedestrians. These patterns can be used for organizational measures to prevent road accidents.

Significant Law Amendments To Prevent Road Accidents In Rural India – Motor Vehicle Act

India is an agricultural country consisting 67% of rural area geographically. Agriculture and its confederated conditioning act as the main source of livelihood for further than 80% population of pastoral India. It provides employment to roughly 52% of labour. Its donation to Gross Domestic Product (GDP) is between 14 to 15. The Government has introduced several schemes and programs that support the lesser robotization of Indian Agriculture, in the light of its commitment to transfigure the husbandry sector and double growers’ income by 2022-23. The Sub Mission on Agricultural Mechanization of (SMAM) is an important action of the Government, in this direction. The country is developing day by day and inventions also take place with a growing country, we also developed it by using innovative products which are discovered for our ease. We’ve to remain in sync with inventions to remain up with current times and make full use of them. At present, demands are different compared to the demands nearly a decade ago. For producing high-quality products growers have to keep pace with changing times. For perfecting your productivity in fields you have to use tractor tools for scattering, tending, digging etc. This application can increase your effectiveness, save lots of plutocrats, saves time etc. so, Tools are introduced for the comfort of the growers. We’re noticing the top 10 tools which are perfect for you to be used with your tractor, they can help you to increase your productivity and effectiveness in the fields. They’re Rotavator, Cultivator, Plough, Trailer, Harrow, Roto seed Drillmaster, Planter, Baler, Sprayer, Straw Reaper etc. All these tools are used by a tractor. Again tractor has their own subtypes similar to Utility tractors which we see generally in large, Compact Tractors, Row Crop Tractors, Industrial Tractors, Garden Tractors, and Carrier Tractors, Earthmoving tractors, Autonomous, two-wheeler type etc. On par with these machinery, growers its confederated sectors like Major Irrigation, Medium Irrigation and Minor Irrigation, PanchayatRaj, Rural Development, Agriculture, Other civil works in pastoral areas use majorly Bulldozers, Excavators, Backhoe, Graders, Trenchers, Payload, Cranes, Compactors, Dump Exchanges, Boring Machines, Wheel Tractor Scrapers etc. in large. All these types of Machines (or) (and) Vehicles are specially designed and manufactured for respective purposes. Still, the operation of said Machines (or) (and) Vehicles is fleetly increased in the last two decades and it’s going to be important large in near future.

CNN Based Driver Drowsiness Detection System Using Emotion Analysis

The drowsiness of the driver and rash driving are the major causes of road accidents, which result in loss of valuable life, and deteriorate the safety in the road traffic. Reliable and precise driver drowsiness systems are required to prevent road accidents and to improve road traffic safety. Various driver drowsiness detection systems have been designed with different technologies which have an affinity towards the unique parameter of detecting the drowsiness of the driver. This paper proposes a novel model of multi-level distribution of detecting the driver drowsiness using the Convolution Neural Networks (CNN) followed by the emotion analysis. The emotion analysis, in this proposed model, analyzes the driver’s frame of mind which identifies the motivating factors for different driving patterns. These driving patterns were analyzed based on the acceleration system, speed of the vehicle, Revolutions per Minute (RPM), facial recognition of the driver. The facial pattern of the driver is treated with 2D Convolution Neural Network (CNN) to detect the behavior and driver’s emotion. The proposed model is implemented using OpenCV and the experimental results prove that the proposed model detects the driver’s emotion and drowsiness more effectively than the existing technologies.

Reasons and Strategy for Road Accident Prevention in Constantine State

Reasons and Strategy for Road Accident Prevention in Constantine State

SÜRÜCÜLER İÇİN DERİN ÖĞRENME TABANLI YORGUNLUK VE UYUŞUKLUK TESPİTİ

Falling asleep while driving is a major part of road accidents. Traffic accidents can be considered as a public health problem and several factors like drugs, driving without rest, sleep disorders, alcohol consumption affect sleep deprivation. Furthermore, drivers are also unaware of falling asleep situations, such as highway hypnosis. All these factors cause accidents while driving and are often fatal. A good background should be provided for drivers to implement effective driver warning systems and other countermeasures just before the accident. In this study, Long Short-Term Memory (LSTM) deep learning based driver warning system has been proposed to prevent road accidents. The Electrocardiogram (ECG) signals of the drivers are processed instantaneously to check whether they go into sleep or not. Experimental studies have been carried out on two different human data sets as sleep mode and awake mode. The simulation results confirm the effectiveness of the proposed method and show its superiority over other state-of-the art methods.

Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV

The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.

World experience of legal regulation of road safety

The article analyzes the characteristics of the legal regulation of road safety in different countries of the world. In addition, current issues in the field of road safety are revealed, which can be conditionally divided into several groups. Such issues include legal regulation; the structural organization of legal regulation: personnel, material, technical and financial support. The key directions of the state policy of different countries of the world in the field of ensuring road safety are identified, as well as new principles for the formation of criteria to guarantee road safety. It analyses the positive experience of some countries in the field of road safety. In particular, the experience of China, Japan, and some European countries in the field of ensuring road safety could be considered by other countries at the same time as the legal regulation of road safety. It is concluded that, to improve the efficiency of the legal regulation of road safety, a systematic approach is required, including the development of objective indicators, the use of systematic and evidence-based measures aimed at preventing road accidents and reducing the severity of their consequences.

АНАЛІЗ СЕЗОННИХ ЗМІН АВАРІЙНОСТІ НА АВТОМОБІЛЬНИХ ДОРОГАХ ДЕРЖАВНОГО ЗНАЧЕННЯ ТА ЇХ ВПЛИВ НА СТАТИСТИКУ ДТП

In the modern world, where more and more vehicles appear every day, the issue of traffic safety is becoming more acute. The most prospective method of traffic safety management, on our opinion, is the analysis of statistical data on road accidents (hereinafter accidents), which allows to determine the accident levele, causes of accidents, evaluate the effectiveness of implemented measures to improve traffic safety and more. As the main task of traffic safety ensuring is to reduce accident level and severity of the accidents consequences, and especially to reduce accidents with fatalities to a minimum, the analysis of accident statistics data plays a key role and is an integral part of a comprehensive approach to solve the issue of the traffic safety management. Qualitative analysis of accidents by various indicators allows determining the main causes that impact on the occurrence of accidents and the severity of their consequences. This article considers the formation of characteristics of seasonal changes in accident level on the network of state roads of Ukraine. The study allows identifying the features and patterns of annual dynamics of road accidents with the definition of monthly maximum and determining the features of seasonal stability of accident level indexes. Taking into account the seasonal variability of the accident level allows rationally planning the activities of transport organizations directed on preventing road accidents and reducing the severity of their consequences. Keywords: accident level, analysis of seasonal changes of accident level, traffic safety, statistics of road accidents.

Estimating the potential of a warning system preventing road accidents at pedestrian crossings

Estimating the potential of a warning system preventing road accidents at pedestrian crossings

Safety Embedded System Integration in Automotive Network CAN Bus to Prevent Road Accidents

In recent years, safety driving has attracted growing interest from the scientific and the industrial communities. This interest is meant to reduce the road accidents caused by driver behavior, fatigue, drugs, etc. Drowsy driving is one of the first factors of road accidents; it compromises driving ability by reducing alertness and attentiveness, delaying reaction times, etc. In this paper, a solution to prevent road accidents based on driver drowsiness detection that can be connected to the other Electronic Control Units (ECUs) in the vehicle network through the Controller Area Network Protocol (CAN) Bus to act on the functionalities of the vehicle when the driver starts feeling (yawning) or become drowsy has been proposed. The proposed solution has achieved an average accuracy of 99.10%. The simulation results confirm that the proposed system can efficiently detect driver drowsiness and warn the driver before anything undesired happens in order to ensure a sufficient and acceptable level of safety. A comparison with recently published works in this field is also provided.

Development and Validation of a New Questionnaire Assessing Women Perception on Malaysian Road Environment (WPRE)


 
 
 High mobility rate among women has made them more vulnerable in the road environment. Lifestyle changes have urged many women to increase their mobility due to accommodate current demand. Therefore, women are exposed to the risk of accidents as many of them are populated in the road environment. However, most studies and instrumentation on-road environments are universal and not specifically targeting women's perception and anticipation preventing road accidents. Hence, the current study is developing and validating instrumentation of women's perception in Malaysia Road Environment. The sample of this study is 93 women with various age numbers. Out of 7 constructs, 6 were found most reliable and valid with the Cronbach Alpha value > 0.75. The present research provides details of factor analysis results, composite reliability, average variance extract, and reliability analysis which all concluded that the internal consistency of WPRE was not violated. Results reveal items developed are suitable to be adapted in future research with some modification. Finally, this research contributes to developing and validating women’s perception in a road accident which is reliable and valid for measuring WPRE.
 
 

Prevention of injuries in concurrent collision of vehicles in AIC technological pro-cesses

The results of research on the prevention of road accidents when carrying out cargo transportation of agro-industrial products as a result of collisions of vehicles moving in the same direction or hitting a standing vehicle or an obstacle are given. It is noted that this situation occurs due to the density of traffic on general roads, as well as on field and country roads characteristic of agricultural production for reasons of low adhesion coefficient in rainy weather, snowfall, ice. In such conditions, a vehicle moving in a stream behind the leader (especially low-clearance ones, such as cars) collides with the rear of the upcoming high-sized transport. The consequences of these collisions are the destruction of the front of the transport moving behind the leader and the injury to the driver and passengers (especially those sitting in the front seat). In order to avoid such situations, an innovative solution is proposed that automatically eliminates the possibility of injury and damage to the rear of the leader and the front of the vehicle colliding with him moving behind the leader.

Prevention of road accidents due to drowsiness detection using PERCLOS algorithm

A Road traffic accident can be characterized as, ‘An occasion that happens on a road open traffic; bringing about at least one person being damaged or murdered, wherein any event one moving vehicle is included. In this way, a road accident is a crash between vehicles; among vehicles and people on foot; among vehicles and creatures or vehicles, and geological or design deterrents. Every year, many road accidents occur due to the drowsiness transpired to the driver. Sleepy driving can be a hazard to any individual who gets in the driver’s seat of a vehicle. It is the reason for at any rate 100,000 vehicle crashes every year. Subsequently, the primary thought behind this paper is to build up a non-meddling framework that can distinguish the languor of the driver and issue an auspicious cautioning to counteract the mishaps and thusly set aside cash and diminish individual anguish. This framework works by consistently observing the eyes of the driver and by utilizing the level of eyelid conclusion after some time (PERCLOS) calculation.

Intelligent Transportation System Based Vehicle-To -Vehicle Communication

Now a day’s vehicle come with lots of features. One of the essential feature which is need to be added is vehicle-to-vehicle communication. Which is mostly usable for night time and to prevent road accidents. Many time a situation comes when an obstacle on road suddenly come in front of vehicles. And before driver could control the vehicle, here the chances off accidents may occur. To overcome this problems vehicle to vehicle communication system installed. It provides automatic communication between nearby vehicles. It does by sensing the obstacles present near the vehicles . By this number of accidents can be reduced during night time on highways, so you can drive safely with heavy vehicles also.

IOT BASED APPROACH FOR REMOTELY MONITORING AND ALARMING A DROWSY DRIVER

There are a lot of road accidents that occur due to drowsy driving. Drowsy driving is when the driver of a vehicle is found to be sleepy and probable to get into a car crash because of the same. Being drowsy might cause the driver to lose concentration from the road, and also reduce the reaction time. Statistics suggest how thousands of deaths and crashes happen every year due to it. Major victims of such crashes tend to be the commercial drivers who need to drive long distances overnight. Our project intends to propose a solution to this problem by providing an Internet of Things based approach. This approach monitors the driver’s face while he or she is driving the vehicle and in case if the driver is to be found falling asleep, an instant voice call is made to the driver’s registered phone number. Additionally, a text message is also sent to the driver’s emergency contact which will get him/her notified and provide the driver with quick assistance if needed. This approach is unique and different in its own way as it provides cross platform support and remote monitoring of the driver. Additionally, it also makes drowsy-detection ‘device independent’. It offers a simplified mechanism to derive real time accurate results and readings with reduced complexities. This project does have a lot of scope, especially considering that there is a lack of methodologies currently being implemented to prevent road accidents due to drowsy driving. KEYWORDS- Drowsy Driving, Monitoring, Machine Learning, Internet of Things, Remote, Algorithm, Eye Aspect Ratio, Python.

Driver Drowsiness Detection Based on Face Feature and Perclos

Driving vehicles are complex and require undivided attention to prevent road accidents. Fatigue and distraction are a major risk factor that causes traffic accidents, severe injuries, and a high risk of death. Some progress has been made for driver drowsiness detection using a contact-based method that utilizes vehicle parts (such as steering angle and pressure on the pedal) and physiological signals (electrocardiogram and electromyogram). However, a contactless system is more potential for real-world conditions. In this study, we propose a computer vision-based method to detect driver's drowsiness from a video taken by a camera. The method attempts to recognize the face and then detecting the eye in every frame. From the detected eye, iris regions for left and right eyes are used to calculate the PERCLOS measure (the percentage of total time that eye is closed). The proposed method was evaluated based on public YawDD video dataset. The results found that PERCLOS value when the driver is alert is lower than when the driver is drowsy.

Detection and Warning System for Motorcycle Vehicular Collision Avoidance


 
 
 World Health Organisation has determined that South-East Asian region has the highest road traffic death in 2015 involving motorised 2-3 wheelers. Generally, motorcycles are lack of active safety features that capable to prevent road accident. Therefore, this study aims to design and develop active safety features for a motorcycle to prevent a frontal collision through an audible warning to the motorcyclist and detected object when the collision is about to happen. The system should consist of a simple algorithm with a minimum set of hardware. Based on the design criteria for the system, an audible warning will only be given when all three conditions stated below are met. (1) An object is within stopping distance limit, (2) object heading towards the centre of motorcycle and (3) relative velocity of an object might cause a frontal accident. The system called Detection and Warning System for Motorcycle Vehicular Collision Avoidance (DEWAMCA). It is expected that DEWAMCA can assist currently available Blind Spot Detection System (BSDS) to reduce accident involving motorcycles significantly.
 
 

VANET: Framework, Challenges and Applications

Abstract: Vehicular Ad-Hoc Network (VANET) is one of the essential research field because of large increase in usage of vehicles on road. Many VANET applications are employed to improve road safety conditions, vehicle traffic, an emergency warning to vehicle drivers, collision avoidance and other non-safety applications for comfort. The main aim of these applications are to modernize the various processes associated with road traffic, vehicles, drivers, passengers and pedestrians by implementing smart transport systems. This study is to implement an intelligent vehicular transport design to improve the road safety, navigation and comfort. VANET communication technology is a combination of mobile vehicles with ad hoc networks. The purpose of this research is to predict and prevent road accidents by transmitting emergency messages using vehicular network technology. While transmitting emergency messages, the VANET has various challenges. They are High mobility of the vehicles, Dynamic topology, Wireless communication, Minimum transmission delay, Connectivity of the network, optimal usage of transmission power. These challenges leave the vehicular networking disconnected and make an exchange of information very difficult. Even though many researchers have done significant work during the last decade on vehicular networking, some problems have not yet been solved.