Accident Avoidance Research Articles

Comprehensive Systems Review for Accident Detection, Prevention, and Real-Time Alerting Mechanisms

The growth of technology and infrastructure at a rapid pace has made our lives easier. The increased frequency of road accidents and possible traffic dangers brought about by newer technologies has resulted in a significant loss of life and property due to inadequate emergency facilities and preventive measures. The greatest hazard to human life is traffic accidents. High speeds combined with intoxication are the main causes of many accidents. Vehicle safety and accident prevention and detection systems are important because they reduce the number of accidents and the time it takes for an accident to be recognized, allowing rescue personnel to attend to injured victims more quickly. This article presents the MCU-designed Vehicle Tracking and Accident Detection system along with several accident avoidance strategies.A prototype of an accident prevention & detection system using a vibration sensor as the detector and an ultrasonic sensor, or IR sensor, is to detect a signal in case of a near collision or a distance between two vehicles to prevent an accident. Even after this, if an accident occurs, they send a signal to the connected microcontroller via the vibration sensor. When the signal is received, the controller activates the relay, causing the airbag to deploy and the brakes to automatically lock. Meanwhile, a message is sent to the nearest help center, so ambulance service and other aid can come in the quickest time feasible.A black box gathers driving information before, during, and after a crash about the vehicle in which it is installed. The data collected by the black box includes speed, braking, acceleration, steering, and airbag deployment. The help can be provided in areas like vehicle safety, collision victim care, insurance corporations, vehicle crash investigations, and improving road conditions through the automotive black box system, which can reduce death rates to a significant level. The main aim of this paper is to include the overview and basics of certain techniques utilized to prevent, detect accidents, and alert the nearby help centers for the fastest aid in case of an accident.

The effect of spatial non-uniformity on multiple transient modes of detonation onset in a three-dimensional channel

The study of the transient combustion modes is one of the key topics when considering the safety of space flights. Control of detonation onset has a dual application. First, the search for ways to prevent detonation modes in case of accidental fuel releases for fire safety issues of launch systems and the avoidance of accidents with rocket engines at a launch site and in near-Earth space. Second, the study of detonation and the possibility of using it to create propulsion systems based on detonation combustion of fuel. The paper shows the effect of the presence of spatial non-uniformities on the promotion of detonation in the chamber. Various geometries with and without obstacles and cavities are considered. It is demonstrated that the presence of obstacles accelerates the transition to the detonation process on the one hand, but on the other hand the presence of obstacles in combustion chamber could be the cause of incidental uncontrolled ignition, which ruins stable operation of an engine. The results of theoretical studies of the working cycle of the combustion chamber of a pulsed detonation engine are presented. Theoretical estimates for thrust characteristics are obtained.

Accident Prevention and Alerting System

The project addresses the increased demand for vehicle tracking systems in the embedded systems industry by providing a solution that combines accident avoidance and real-time tracking capabilities. The system takes a holistic approach to road safety by integrating components such as the Arduino microcontroller, alcohol sensor MQ2, heartbeat sensor, limit switch, relay, Liquid Crystal Display, Global Positioning Satellite, and Global system for mobile communication module. The system's ability, in particular, to detect and prevent drunk driving incidents makes a substantial contribution to road safety. The project's goal is to avoid accidents and fatalities by automatically alerting drivers when they attempt to operate a car while intoxicated. Finally, the project's emphasis on improving road safety through proactive monitoring and intervention highlights its potential to make a significant contribution to reducing traffic accidents and saving lives.

Travaler Empowerment through Iot - Based Wearable Health Monitoring Glove

The Travel Empowerment with IoT-Based Wearable Health Monitoring Gloves for Accident Prevention is a cutting-edge device that uses the Internet of Things (IoT) to increase road safety and empower travelers. This innovative system includes a wearable device that resembles gloves and is equipped with state-of-the-art sensors, including GPS, GSM, ultrasonic, infrared (IR), and a heart rate sensor. The intention behind incorporating these sensors is to provide travelers with a comprehensive safety net. The gloves' GPS and GSM components provide precise position monitoring and a real-time communication with emergency services. By using this service, travelers may be sure that assistance will be dispatched right to their area in the case of an accident or tragedy. The passenger receives an extra layer of safety from the ultrasonic sensor, which proactively prevents accidents by detecting potential hazards or blockages in the traveler's immediate surroundings. By identifying any hazards that the human eye might overlook, the infrared sensor enhances the safety system even more. This comprehensive hazard detection system improves overall travel safety as well as the prevention of accidents. Additionally, a heart rate sensor included into the gloves monitors the wearer's physiological well-being over time. In the case of an emergency while traveling, the technology may promptly send out alarms in the event of aberrant heart rate patterns. The Travel Empowerment system gathers data in real-time from various sensors. The gathered data is subsequently processed by a clever algorithm. In the case of an anomaly or emergency, the gloves instantly sound a warning, providing vital information to emergency contacts, including the traveler's whereabouts, the severity of the issue, and their heart rate. This Internet of Things (IoT)-based wearable health monitoring glove system not only addresses the urgent problems of emergency response and accident avoidance, but it also allows users to track their health in real time while traveling. By enabling a complete approach to travel safety, the combination of GPS, GSM, IR, ultrasonic, and heart rate sensors gives customers a more empowered and secure travel experience.

Deep investigation of muography in discovering geological structures in mineral exploration: a case study of Zaozigou gold mine

Summary Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements, and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analyzed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel three-dimensional reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefiting from the seed algorithm, a low-density-contrast orebody and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field.

An advanced accident avoidance system based on imbalance-control ensemble and deep learning fusion design

The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of this paper is to design an advanced Accident Avoidance System established on a deep learning and ensemble fusion strategy in order to acquire more performant crash predictions. As such, four highly optimized models for crash prediction have been designed based on the popular ensemble techniques: CatBoost, AdaBoost and Bagging and the deep learning CNN. Additionally, four categories of features, including driver inputs, vehicle kinematics, driver states and weather conditions, were measured during the execution of various driving tasks performed on a driving simulator. Moreover, given the infrequent nature of crash events, an imbalance-control procedure was adopted using the SMOTE and ADASYN techniques. The highest performances results have been acquired using CatBoost along with ADASYN on almost all the adopted metrics during the different weather conditions, and more than 50% of all crashes have occurred in rainy weather conditions, whereas 31% have been exhibited in fog patterns. The sensitivity analysis results indicate that the fusing all the acquired features has the highest impact on the prediction performance. To our knowledge, there has been a limited interest, if not at all, at adopting a fused ensemble deep learning system examining the real-time impact of the adopted features’ combinations on the prediction of road crashes while taking into account class imbalance. The findings provide new insights into crash prediction and emphasize the relevance of the explanatory features which can be endorsed in designing efficient Accident Avoidance Systems.

Enhancing Privacy in the Internet of Vehicles via Hyperelliptic Curve Cryptography

The Internet of Things (IoT) is a technological paradigm that has gained significant momentum the last decade and, among other features, enables the development of intelligent and interoperable device networks. In this respect, it has triggered the creation and evolution of vehicular ad-hoc networks (VANETs), which are initially implemented in order to guarantee the safety of drivers and the avoidance of traffic accidents. The drawback is that this fast evolution comes with serious concerns in terms of the privacy of users, while the population of attackers or entities that try to eavesdrop and intercept information has significantly increased. This imposes a serious risk for drivers moving across a Smart City. The research presented in this paper aims to evaluate privacy protection mechanisms in VANET environments, based on the efficiency and security level they ensure, considering the fact that VANETs provide limited resources to users/drivers. Moreover, the usage of elliptic curve cryptography in reduced resources environments is discussed. Finally, this paper compares the performance of three cryptographic algorithms, elliptic curve cryptography (ECC), hyperelliptic curve cryptography genus 2 (HECC-2) and HECC genus 3 (HECC-3), employed for an efficient authentication and safe message transmission mechanism in VANETs, aimed at reaching conclusions related to the implementation of each cryptographic scheme in this specific application area. The evaluation results indicate that ECC supersedes HECC-2 and HECC-3 in most metrics. However, HECC-2 and HECC-3 demonstrate better responses than ECC does in selected energy metrics. Overall, it is observed that HECC algorithms are not yet mature enough to compete with ECC. This is due to the fact that the research community has not sufficiently progressed toward the optimization of HECC, and moreover, HECC builds on quite complex mathematics. There are indications, however, that once HECC curves are indeed optimized, HECC will outperform ECC in speed as well as in other metrics, sinceHECC-2 and HECC-3 use a significantly smaller key size with the same level of security as that of ECC.

Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry

Building and repair of ships are considered the Evergreen industry nationally as well as globally. The ships are generally gone in by the periodic scheduled repairs by the Indian shipbuilding industries. Sometimes industries lack productivity and lack of modernization some modern methods should be followed. The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy. The selected data set is pre-processed and feature selection for the optimization for the improvement in accuracy, and automation decision making the framework of the convolution neural network along with the ensemble boosted tree classifier developed is optimized using the jellyfish optimization and Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model for the recognition of patterns and numerical vectors in the real-world data after processing of output then it is sent back as the input for the recurrent network to make the decision in the shipbuilding process. By evaluating the performance results and confusion matrix through the training and testing output all the metrics for training and testing are classified in the confusion matrix. Our proposed predictive maintenance model with high accuracy for the detection of failures in earlier stages and maintenance of Indian ships can help in the avoidance of accidents in voyages and the loss of goods and money during transportation. The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models. The proposed Pd-MaaS helps in early detection of failures in the ships which is the greatest advantage in the Indian shipbuilding industry.

Safety Performance Assessment via Virtual Simulation of V2X Warning Triggers to Cyclists with Models Created from Real-World Testing

The overall crash statistics in the EU still show a very significant number of car–cyclist crashes. Within the Horizon 2020 project Safe-Up, countermeasures have been developed to reduce this number. One of these countermeasures involves a V2X-enhanced on-board unit for cycles, which can provide on-time warning triggers. The research assumption was based on studying the benefits of connectivity in enhancing cyclists’ safety. This study assessed the performance of this potential technology both qualitatively by analyzing volunteer feedback during physical testing and quantitatively by virtual simulations. The volunteers’ study showed positive findings on system’s safety relevance, user experience, and user acceptance. The method applied for the virtual simulation is a prospective safety performance assessment with reconstructed accident scenarios based on the GIDAS database and cyclist behavior models, obtained from physical testing. The results using a warning trigger 4 s prior to the collision showed a potential safety benefit of approximately 98%. It should be noted that this trigger time was found to be quite early in both physical testing and virtual simulation. Further research is required to evaluate the system’s performance in more complex urban scenarios, as well as to design the human–machine interaction strategies for optimal accident avoidance.

Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior.

Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.

Real Time Alert System for Slumberous and Fatigue Driver Using Eye Blink Sensor

Various technologies are being created to reduce accidents, which are rising rapidly in the modern day. Driver weariness or drowsiness is a significant factor in the occurrence of accidents. Drowsiness is detected using a variety of deep learning and image processing techniques. However, these techniques are pricey, and they might not work for those who wear glasses. This suggested solution employs an eye blink sensor to stop drowsiness-related accidents. This serves as an accident avoidance mechanism. The eye blink sensor-equipped goggles that the driver is required to wear include these features.To detect driver drowsiness, the goggles must be worn by the driver the entire time they are operating a vehicle and must blink for a few seconds. When the alarm system detects that the driver is dozing off, it will sound an alert until they open their eyes. Along with detecting drowsiness, the LDR sensor is used to control the high intensity of the headlights when two vehicles are approaching from opposite directions. Additionally, it helps to avoid accidents caused by blurry vision. When a driver snoozes more than five times, the GSM modem alerts the vehicle's owner. In this situation, the both owner and the driver could be alerted when the driver is drowsy.

Time-Series Transfer Learning: An Early Stage Imbalance Fault Detection Method Based on Feature Enhancement and Improved Support Vector Data Description

Early stage fault detection plays a pivotal role in Industrial equipment accidents avoidance and scientific maintenance. While limited by the complex operation background, its application encounters with the conundrum of fault feature indistinctness. To address the challenge, a time-series transfer learning (TSTL) method is proposed, which contains two phases: first, early stage series are transferred to their corresponding serious stage for fault feature enhancement. Moreover, due to the improvement of model structure and loss function, the limitation of mismatched working condition is well-weaken. Second, a transferred fault mode recognition model is trained by using transferred normal series that provides a novel solution for data imbalance. Finally, the TSTL method is verified by actual vibration datasets of power pole tower bolts. Its superiority in feature transfer and fault detection is confirmed by several groups of comparative experiments and results demonstrate TSTL outperforms mainstream methods.

Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning

With the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF) and coordination treatment for two networks will greatly improve system performance, reduce system complexity, and improve the precision and control ability of both networks. Situation awareness (SA) is the key function for DF and accident avoidance for both networks with different network structures, data types, system mechanisms, and so on. This paper use tensor computing to provide a general data model for heterogeneous and multidimensional big data generated from smart grid and power communication network. A novel data fusion scheme is designed with multidimensional tensors. Deep reinforcement learning (DRL) algorithms are utilized to construct an optimal SA strategy based on tensor big data. A multi-agent actor-critic (MAAC) algorithm is used to achieve an optimal SA policy and improve system performance. The proposed DF and SA schemes based on tensor computing and DRL provide useful guidance for smart grid and power communication networks from theory and practice.

Active and passive safety systems (not only) for senior citizens : Systems to increase road safety

Due to a constantly increasing life expectancy and the need to stay mobile at a high age, the proportion of over 65-year-olds in traffic accidents is increasing from year to year. In order to identify potentials for increasing the safety of seniors in road traffic, accident data were analyzed with respect to the categories of road users and accident types of this age group. Based on the accident data analysis, active and passive safety systems are described that could increase the road safety particular for senior citizens. It was found that older road users are frequently involved in accidents as car occupants, cyclists and pedestrians. In addition, car drivers and cyclists aged 65 years and older are frequently involved in driving, turning and crossing accidents. Lane departure warning and emergency braking assistants have ahigh potential for accident avoidance as they can defuse critical situations at the last moment. Restraint systems (airbags, seat belts) adapted to physical characteristics could minimize the severity of injuries to older car occupants.

Predictive Vehicle Safety—Validation Strategy of a Perception-Based Crash Severity Prediction Function

Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehicle accidents in the near future. Innovative Pre-Crash systems (PCS) should contribute to the accident mitigation of unavoidable accidents. However, there are no standardized testing methods for Pre-Crash systems. In particular, irreversible Pre-Crash systems lead to great challenges in the verification and validation (V&V) process. The reliable and precise real-time crash severity prediction (CSP) is, however, the basic prerequisite for irreversible PCS activation. This study proposes a novel validation and safety assessment strategy for a perception-based crash severity prediction function. In doing so, the intended functionality, safety and validation requirements of PCS are worked out in the context of ISO 26262 and ISO/PAS 21448 standards. In order to reduce the testing effort, a real-data-driven scenario-based testing approach is applied. Therefore, the authors present a novel unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs based on K-Means++ and k-NN algorithms. The developed methodology is used on the GIDAS database to extract 35 representative clusters of car to car collision scenarios, which are utilized for virtual testing. The limitations of the presented method are disclosed afterwards to help future research to set the right focus.

Evaluating impact of remote-access cyber-attack on lane changes for connected automated vehicles

Connected automated vehicles (CAVs) rely heavily on intelligent algorithms and remote sensors. If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication, it can cause significant damage to CAVs or passengers. The primary objective of this study is to model cyber-attacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment. Based on the analysis on environmental perception system and possible cyber-attacks on sensors, a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed. The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lane-changing process is then quantitatively analyzed. The evaluation indexes include spatio-temporal evolution of average speed, spatial distribution of selected lane-changing gaps, lane-changing rate distribution, lane-changing preparation search time, efficiency and safety. Finally, the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack. Also, when the traffic system is under cyber-attack, more unsafe back gaps are chosen for lane-changing, especially in the center lane. Therefore, more lane-changing maneuvers are concentrated on approaching the off-ramp, causing severe congestions and potential rear-end collisions. In addition, as the number of cyber-attacked vehicles and the severity of cyber-attacks increase, the road capacity and safety level will rapidly decrease. The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.

Vehicles Exhaust Smoke Detection and Location Tracking

Abstract: In recent years, automatic vehicle monitoring has become a crucial situation that canbe made achievable by using the following technology. Environmental awareness was givenprominence at the start of the twenty-first century.One of the primary issues with the Air pollution is the environment. The main pollutants produced byautomobiles are carbon and nitrogen oxides, whichcan now be quickly found using semiconductor gassensors. The system is made up of a GSM board, anIoT module, and a GPS board. The GPS system communicates the co-ordinates to the local RTO when the gas sensor detects the gas created as a result of excessive pollution. The micro-controller board calculates the proportion and, if it is higher than the stated limit as per Bharat Stage 6 rules, it determines the proportion. The received warnings will be saved, and the car will automatically give guidance to a nearby safe zone if the warnings are received more than three times or if the pollution level significantly increases. IoT and GPStechnologies are used to automatically identify the safe zones. If this technology is used, it is possible to meet the safety, pollution reduction, and accident avoidance criteria.

Real-Time Drowsiness Identification Based On Eye State Analysis

As per the previous year’s report concerning to road crashes indicates that the principal cause of such a fatal road accidents is because of negligence behavior as well as drowsiness of driver. This problem reveals the requirement of such a system that can recognize drowsiness state of driver and gives alert signal to the driver before the occurrence of any accidents. Therefore, this proposed work has established drowsy detection as well as accident avoidance system based on the eye blink duration. Here, first the open and close state of eye are detected based on the eye aspect ratio (EAR). Further, the blink duration or count during the changes of eye state from open to close are analyzed. Then, it identifies the state of drowsiness, when blink duration becomes more than a certain limits and sends the alert message to the driver through the alarm. Our developed system has shown the accuracy of 92.5 % approx on yawning dataset (YawDD). Key Word: Eye blink detection; Drowsiness Detection; Eye Aspect Ratio (EAR)

Drowsiness Detection System

A sleepiness detection system uses technology to spot and warn people who might be about to nod off or become less vigilant. To assess the degree of drowsiness, the system uses a variety of physiological or behavioural signs, including eye movements, brain waves, and patterns of vehicle control. Research is now being done to create reliable sleepiness detection systems, which could have positive effects on safety, accident avoidance, and productivity for a variety of businesses and people. These technologies all have the potential to increase safety in a variety of industries and lower the risk of accidents brought on by drowsiness, even though their accuracy and dependability vary. By lowering accident costs and boosting productivity, the deployment of sleepiness detection systems may also have favourable economic effects. Over all, the creation of precise and trustworthy sleepiness detection systems is an important field of study with potential advantages for a range of businesses and people.

Nationwide survey on the current situation of quality control of diagnostic displays in Japan

The Japan Association of Radiological Technologists (JART) and the Japan Medical Imaging and Radiological Systems Industries Association jointly conducted a nationwide survey to reveal the current situation of diagnostic displays in Japan using a questionnaire on the performance and quality control (QC) of diagnostic displays for mammography and common use. The questionnaire for radiological technologists (RTs) was distributed via email to 4519 medical facilities throughout Japan, where RTs affiliated with JART were employed; 613 (13.6%) facilities responded. Diagnostic displays with suitable maximal luminance (500cd/m2 or higher for mammography and 350cd/m2 or higher for common use) and resolution (5 megapixels for mammography) have been widely used. However, while 99% of the facilities recognized the necessity of QC, only approximately 60% implemented it. This situation arose due to several barriers to QC implementation, such as insufficient devices, time, staff, knowledge, and the recognition of QC as a duty. The implementation of QC can lead to the avoidance of incidents or accidents caused by a decrease in luminance, variation in luminance response, and the influence of ambient light. Moreover, the barriers discouraging the implementation of QC are mainly related to a lack of human resources and budgets. Therefore, to popularize the QC of diagnostic displays in all facilities, it is crucial to identify countermeasures to eliminate these barriers and to continue positive actions for popularization.