Accident Avoidance Research Articles

Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation

Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents. Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This paper, therefore, argues in favour of Resilience Engineering using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario on a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation. Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework. Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes show that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation. In conclusion, the FRAM analysis supports decision-makers in enhancing safety enriched by the identification of non-linear and complex risks.

블랙아이스 사전 예측과 능동 속도 제어를 위한 노면 젖음 감지 센서의 활용 방안 연구

This study was performed to verify the feasibility of using a black ice danger area estimation function based on the wetness data acquired by a piezoelectric based RCS (Road Condition Sensor) through real road tests under each environment and real vehicle conditions. The techniques and system implementation are considered for the prevention of accidents caused by black ice, which primarily causes critical issues in winter every year. System detects the value of wetness on the road during (or right after) rain and snow using an RCS then can estimate the possibility of the presence of black ice on the forward path at low temperatures by combining with those obtained using the ambient temperature sensor and GIS (GPS and map). Accident avoidance and damage minimization effect is expected by securing safe driving conditions through active speed control.

A multi-scale convolutional neural network based fault diagnosis model for complex chemical processes

The chemical production process is a special dynamic and complex system. It has the characteristics of instability and danger, thus making safety management in the production process very difficult. To support the long-term and stable operation of the chemical process, timely and accurate fault diagnosis is very necessary. Aiming at the high-dimensional nonlinearity of chemical process data, this paper proposes a fault diagnosis method in chemical process that is based on multi-scale convolutional neural network (MsCNN) combined with matrix diagram. This model uses software to convert the pre-processed time series signal data of the chemical process into sets of matrix diagrams, and then uses the MsCNN model to accurately diagnose various types of faults in the chemical production process. Taking Tennessee Eastman (TE) process fault data set as an example, verification shows good simulation results in detection accuracy and loss. This method can create high-precision judgments on fault types, which is beneficial to timely elimination of faults and avoidance of safety accidents. The results show that the new model proposed in this paper has good potential in the field of fault diagnosis in chemical processes, and is also reliable, practical and scientific.

Advance Fire Control and Detection System

- In recent years, the usage of various domestic Internet of Things (IoT) devices has grown increasingly popular. One required and significant use of home automation with IoT is fire detection and fire accident avoidance. It has been noticed that most of the houses lacks fire detection & control system and Existing systems have too many errors. To solve or overcome this problem, Researcher decided to gather all the data related to fire detection and controlling system and developed a prototype of advance fire detection & controlling system based on IoT and Arduino which is cheap in cost and can be installed easily. User can install this system according to their interest area. This system provides 90% accuracy and doesn’t give false alarms like existing one does. This prototype technology can assist users enhance safety standards by preventing mishaps immediately. So, the future of these systems is bright as new technologies can be applied to improve its accuracy.

Accident avoidance and settlement bargaining: The role of reciprocity

This paper explores how reciprocity concerns of potential accident victims influence potential injurers’ precautions before an accident and victims’ settlement behavior after an accident. In our framework, a strictly liable injurer privately informed about the probability of losing in trial chooses either high or low care and, in the event of an accident, the victim demands a settlement amount from the injurer. We find that victims’ intention-based reciprocity concerns tend to increase both the level of precautions and the likelihood of trial and that the impact of reciprocity concerns on equilibrium outcomes depends on the legal cost allocation rule.

Impact of Safety Culture Implementation on Driving Performance among Oil and Gas Tanker Drivers: A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach

This research aims to develop a safety culture model by investigating the relationship between safety culture and driving performance. In previous studies, safety culture has been one of the factors that determine safety issues. These issues were then contextually transformed via a pilot study and organized in the form of a theoretical model. The data were collected from 307 oil and gas tanker drivers in Malaysia through questionnaire surveys. Consequently, structural equation models of partial least squares (PLS-SEM) were applied to statistically assess the final model of this study. The results showed that the implementation of safety culture contributes to driving performance at a substantial level; there is a strong association with an effect of 67.3%. The findings of this research would serve as a benchmark for decision-makers in the oil and gas transportation sector, as promoting an awareness of safety culture should boost the efficiency of drivers. This research fills a gap in knowledge by identifying that positive safety culture practices and mindset are direct antecedents for the improvement of driver performance and, thus, the avoidance of road accidents.

Smart Helmet

Road accidents are increasing day by day because the riders are not using the helmet and due to consumption of alcohol. In today's world, huge numbers of people are dying on road accidents. By using smart helmet, the accidents can be detected. The main target of the project is designing a smart helmet for accident avoidance and alcohol detection. The features of the Smart Helmet are wearing helmet, detection of alcohol, detection of accident. If the person is not wearing helmet the bike will not start and it displays on the LCD. The alcohol sensor recognizes the alcoholic substance in the rider's breath. If the person is drunk then alcohol sensor detects it and displays on the LCD and the bike will not start. If there is no detection of alcohol then the bike starts. If there is no sign of alcoholic substance present and helmet is used, only then the bike will start. The bike will not start if any of the two conditions is violated. If an accident is occurred then it is detected by Vibration sensor and alarm is activated. If the person is in conscious state, then the person can off the alarm and no message is sent to the saved numbers. If the person is in unconscious state, then message will be sent to the saved numbers in GSM module and sends the location to them by GPS. With the use of Smart Helmet, accident rate can be reduced.

Internet of Things Based Approach to Detect Obstacle in Fog to Avoid Accidents

Human life is of the utmost importance. Every year, many people lose their lives in road accidents. Reasons for an accident are many like over-speeding, poor traffic system, drink and drive, rough driving, etc. this paper aims to develop a model that can be used in vehicles to identify accidents due to Zero visibility in fog in winters and alarming for the same to avoid it. IoT is an emerging technology and can be intensively used for intelligent transport systems. This paper aims to develop an automated IoT-enabled system to be used in cars for the detection and avoidance of accidents due to zero visibility in fog. To build this system, the Ultrasonic sensor will be integrated with Raspberry Pi along with other supportive components (power-bank, Jumper wires, breadboard, etc.).

An Innovative Prototype to Prevent Accidents using Eye Blink Sensor, Ultrasonic Sensor, Alcohol Sensor, MEMS Accelerometer

The ever increasing number of major traffic accidents around the world can be significantly reduced if modern technology is incorporated within the automobile to assess the physical condition of the driver as well as the outside situation of the automobile at regular intervals during the movement of the vehicle and preventive measures are automatically taken for the safety of all concerned entities, both within the vehicle and outside the vehicle. In this paper, design of a model incorporated with four sensors, namely, Eye Blink Sensor, Ultrasonic Sensor, Alcohol Sensor and MEMS Accelerometer, is presented which can monitor the physical state of the driver and the outside situation of the vehicle at regular intervals during his/her driving and raise an audible alarm within the vehicle to alert the driver. The prototype uses Eye Blink Sensor, Ultrasonic Sensor, Alcohol Sensor and MEMS Accelerometer to assess the physical condition of the driver and the outside situation of the vehicle and raise an alarm to alert the driver if any sensor senses any change in the normal condition. The prototype of the design has been successfully implemented which leads to the conclusion that such a system can help in keeping the driver alert at all times and monitor the outside situation while driving and hence facilitate in avoidance of any traffic accidents involving driver’s alertness.

Developing a Ship Collision Risk Index estimation model based on Dempster-Shafer theory

A reliable and accurate evaluation of the risk of a ship collision with other vessels is crucial for the avoidance of maritime accidents. The ship operators use the collision risk index (CRI) to detect the risk of a collision and take the necessary action. However, CRI can be assessed differently depending on various operating conditions or other vessels or encounter conditions, making it difficult to calculate such risk accurately and efficiently. In this study, a new method for calculating the CRI by combining machine learning with D-S theory is proposed to increase the efficiency of the computations while preserving the prediction accuracy of the CRI. Different machine learning models have been investigated and compared based on model accuracy and computational time, and the results showed that the gradient boosting regression (GBR) model efficiently estimates the collision risk and increases the calculation speed compared to the D-S theory. Further, the effectiveness of this approach was examined by collision avoidance simulation while simultaneously satisfying the COLREG rule and making early proper decisions to avoid collisions, which shows the advantages of the proposed risk assessment model in practical application.

Intelligent Drive Assistance System

The report proposes the research conducted and the project made in the field of computer engineering to develop a system for driver drowsiness detection to prevent accidents from happening because of driver fatigue and sleepiness. The report proposed the results and solutions on the limited implementation of the various techniques that are introduced in the project. Whereas the implementation of the project give the real world idea of how the system works and what changes can be done in order to improve the utility of the overall system. Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road. A person driving needs to be able to focus on driving at all instances. Any prolonged or sudden complications to the person driving the vehicle can cause serious accidents/damages. To ignore the importance of this could result in severe physical injuries, deaths and economic losses. Road incidents remain the leading type of fatal work-related event, carrying tremendous personal, social, and economic costs. While employers with a fixed worksite can observe and interact directly with workers in an effort to promote safety and reduce risk, employers with workers who operate a motor vehicle as part of their job have fewer options. Drowsiness detection system is regarded as an effective tool to reduce the number of road accidents. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. Developing various technologies for monitoring and preventing drowsiness while driving is a major trend and challenge in the domain of accident avoidance systems. Haar face detection algorithm is used to capture frames of image as input and then the detected face as output.

Internet of Vehicles: Architecture, services, and applications

SummaryThe connection between objects and information exchange has been possible in recent years, with the advent of the Internet of Things (IoT) in different industries. We can meet different requirements in each industry utilizing this feature. Intelligent transportation uses the Internet of Vehicles (IoV) as a solution for communication among vehicles. It improves traffic management applications and services to guarantee safety on roads. We categorize services, applications, and architectures and propose a taxonomy for IoV. Then, we study open issues and challenges for future works. We highlighted applications and services due to drivers' requirements and nonfunctional requirements, considering the qualitative characteristic. This paper summarizes the current state of the IoV in architectures, services, and applications. It can be a start view to provide the solutions for challenges in traffic management in cities. The present study is beneficial for smart city developments and management. According to this paper's result, the services and applications evaluate performance with 34% frequency, safety and data accuracy, and security with a 13% frequency in selected papers. These measurements are essential due to the IoV characteristics such as real‐time operation, accident avoidance in applications, and complicated user data management.

Are Regulations Safe? Reflections From Developing a Digital Cancer Decision-Support Tool.

PURPOSEInformatics solutions to early diagnosis of cancer in primary care are increasingly prevalent, but it is not clear whether existing and planned standards and regulations sufficiently address patients' safety nor whether these standards are fit for purpose. We use a patient safety perspective to reflect on the development of a computerized cancer risk assessment tool embedded within a UK primary care electronic health record system.METHODSWe developed a computerized version of the CAncer Prevention in ExetER studies risk assessment tool, in compliance with the European Union's Medical Device Regulations. The process of building this tool afforded an opportunity to reflect on clinical concerns and whether current regulations for medical devices are fit for purpose. We identified concerns for patient safety and developed nine practical recommendations to mitigate these concerns.RESULTSWe noted that medical device regulations (1) were initially created for hardware devices rather than software, (2) offer one-shot approval rather than supporting iterative innovation and learning, (3) are biased toward loss-transfer approaches that attempt to manage the fallout of harm instead of mitigating hazards becoming harmful, and (4) are biased toward known hazards, despite unknown hazards being an expected consequence of health care as a complex adaptive system. Our nine recommendations focus on embedding less-reductionist and stronger system perspectives into regulations and standards.CONCLUSIONOur intention is to share our experience to support research-led collaborative development of health informatics solutions in cancer. We argue that regulations in the European Union do not sufficiently address the complexity of healthcare information systems with consequences for patient safety. Future standards and regulations should continue to follow a system-based approach to risk, safety, and accident avoidance.

Arduino Based automatic Vehicle Control

The objective of this research is to develop a system to keep the vehicle secure and protect it by the occupation of the intruders. The aim is automatically controlling the speed of vehicle and accident avoidance using arduino and ultrasonic sensors. Arduino mega 2560 is used to create the path through programing. The programed path is fed into the Arduino which is followed by the vehicle. Whenever any obstacle is detected by the running vehicle, depends on distance automatically control the speed of vehicle. The ultrasonic sensor system continuously sends signals and monitors any car or other obstacles are in front of vehicle. The distance up to which ultrasonic sensor can work may be up to 4 m. When any obstacle or vehicle is detected by ultrasonic sensor system, it will send signal to the embedded board. After receiving this signal embedded board sends a signal to the motor to reduce the vehicle speed automatically which can control vehicle speed immediately. Vehicle is controlled automatically without any manual operation when the obstacle is at 4 m distance away from the front vehicle. In critical cases, if vehicle is met into accident GSM send a message to the user.

P2-SHARP: Privacy Preserving Secure Hash based Authentication and Revelation Protocol in IoVs

In the near future, one of the promising concepts that are going to change undoubtedly the whole automobile industry, vehicular networking, and transportation sector is the Internet of Vehicles (IoVs). IoVs open new opportunities in different services and applications to enhance road conditions, driver/passenger safety, transportation issues, non-safety applications, and especially for the avoidance of accidents. For reducing accidents, timely warning messages play an important role. For that, lightweight protocols are needed. Another important criterion to be ensured is ‘security’. Because the attackers always try to convey false warning messages that may result in serious issues or attacks. However, in the existing research, secure and lightweight protocols in IoVs are missing. Hence, in this paper, we designed a protocol, P2-SHARP (Privacy-Preserving Secure Hash-based Authentication and Revelation Protocol) using IoVs. In this scheme, secure transmission of local and global warning messages is done. For ensuring security, we have used several cryptographic operations. Security and performance analysis reveals the efficiency of P2-SHARP.

Driver Distraction Detection and Early Prediction and avoidance of accidents using Convolutional Neural Networks

In this fast-moving world, accidents in four wheeled vehicles occur due to the break failure or because of the carelessness or the fatigue of the driver. The driver pattern of the driver plays a major role in providing road safety as well as in fuel consumption. The distraction of drivers is found by installing various sensors which is used for gathering real time data. The behaviour of drivers under stress condition and their behavioural patterns for early detection and avoidance of accidents are found using convolutional neural networks. Convolutional Neural Networks are efficient classifiers in handling image processing and computer vision problem. The input dataset is a collection of driving behaviour of 10 different drivers collected from Kaggle. The behaviour of drivers under 7 distracted situations like texting, talking through phone, playing music, drinking, eating, doing make up and talking to passenger are considered. The batch normalization is used at the right of the input layer in order to avoid skewing of data at a direction. It is shown, the convolutional neural networks at 4 epochs have shown 99% accuracy.

Internet of things based intelligent accident avoidance system for adverse weather and road conditions

Study shows that road accidents cause nearly 6,000 people to die and more than 400,000 people injured in the United States every year. Adverse weather and road conditions are some of the major reasons that contribute to 22% of accidents. People get injured and sometimes even lose their life due to road accidents that cause physical and mental instability. Regardless of the expertise of a good driver, at some point, an intelligent transportation system is necessary for the vehicle to make an immediate decision to avoid accidents. Road and weather-related mishap are those which occur due to adverse conditions like fog, winds, snow, rain, slick pavement, sleet, etc. Such accidents, though completely unavoidable, but can be reduced to some extent if proper measures are taken. Vehicle velocity, vehicle size, vehicle weight, momentum are a few of the reasons for a vehicle to go out of control. An intelligent accident avoidance system can predict the safe speed of a vehicle according to its size, weight, and momentum in different weather and road conditions. It can reduce the likelihood of accidents related to weather and road conditions. In this paper, we propose an Internet of Things (IoT) based intelligent accident avoidance system for adverse weather and road conditions. The proposed system comprises of an IoT system that perceives the environment for different weather and road conditions and a machine learning-based intelligent system that learns the adverse conditions that influence an accident to predict and suggest safe speed to the driver. The proposed system is experimented with real-time datasets and simulated using the Blynk application.

Economic and social benefits of a strategy of ecological monitoring for oil and gas companies operating in the Russian Arctic

Ecological monitoring provides a more complete picture of the possible effects of extractive activities for oil and Gas companies operating in the Russian Arctic and in other ecological sensitive areas. This paper proposes a framework to evaluate the adequacies of different monitoring systems for Operations in the Arctic for oil and Gas companies. Our research also demonstrates how Ecological monitoring could be a useful strategic tool for long term risk reduction, accident avoidance and to improve the socio-economic development for indigenous communities.

AEB effectiveness research methods based on reconstruction results of truth vehicle-to-TW accidents in China

Vehicle-to-TW accidents are one of the largest components of traffic accidents system in China, which always leads to severe accident consequences because the cyclists are vulnerable road users and drive at a high collision velocity. With the development of information and communication technology, automated Emergency Braking (AEB) system has been applied to modern vehicles, which can perform emergency braking automatically in dangerous situations and mitigate the consequence of accident. The purpose of this study is to propose a method to identify the mechanism that affects the effectiveness of AEB functions under real-life vehicle-to-TW traffic accident scenarios with Chinese county characteristics. Through the analysis and reconstruction for the sampling accident case-by-case, the whole process of the accident has been reproduced. The trajectory analysis program determines the accident triggering conditions and then generates the virtual physical parameters of the triggering conditions through the virtual sample generation method based on the initial sample. In parallel, the AEB effectiveness simulation program has been established. Plug in parameters of AEB system generated by the Latin hypercube sampling to the AEB effectiveness simulation program for getting the mechanism that affect the effectiveness of AEB functions. The AEB system parameters generated by Latin hypercube sampling are inserted into the AEB effectiveness simulation program to obtain the mechanism that affects the effectiveness of the AEB function. Under the multi-objective optimization problem of accident avoidance rate, technical cost and occupant comfort, the optimal parameters and multi-objective values of AEB are obtained by the NSGA-II algorithm. These results can adapt to Chinese actual traffic conditions to a certain extent, and provide a reliable basis for the research and development of AEB system in China.

Driver Response Times to Side Road Path Intrusions from SHRP-2 Naturalistic Database

In an age where all major manufacturers are trying to get a better understanding of when an emergency response should be triggered, it becomes imperative to learn how humans respond to emergency events. If one can understand driver behavior, systems can be designed around the user to either assist drivers where they fail to perform well or completely eliminate them from the accident avoidance maneuver. In this study, 169 crash and near crash events from the SHRP2 dataset were analyzed. The response behavior of drivers was measured in events where the through drivers’ path was intruded upon by another vehicle perpendicular to its path. Overall, drivers responded significantly faster when the other vehicle failed to stop, and at intersection locations.