Collision Prevention Research Articles

Deterrence of Accident Using IoT

Now-a-days human beings have a tendency to transport in a faster pace which leads to greater street injuries. During night time motors typically tour in more velocity. When an coincidence happens in the course of night it is difficult for the rescue team to right away locate the place and shop the humans. This document gives a machine to robotically alternate between high and low beams with the aid of dimming the headlight of the different vehicles. Automatic collision prevention and detection is the device anticipated. It will become aware of a auto crash, and then ship the vital facts to the hospitals. This gadget substantially helps cars that have indulged in an accident in the search and rescue. The pace of the motors is one of the important reasons. If emergency offerings should achieve facts about accidents and attain them early, many lives can be saved. GPS has now end up a part of a vehicle framework. A GPS recipient is successful of checking the vehicle speed and recognizing mishaps dependent absolutely upon oversaw speed and sending an accident region to an Alert Service Center. We have put collectively an automated headlight dimmer prototype to stop such incidents. This adjusts the excessive beam routinely into the low beam, which reduces the depth of light by way of sensing the automobile approaching. It additionally eliminates the want for manual driver switching that is no longer constantly done. This paper talks about in element the building and blessings of this model.

Non-fatal injuries among boys and girls presenting to Red Cross War Memorial Children’s Hospital, Cape Town


 
 
 
 Background. In South Africa (SA), road traffic injuries, homicides and burns are the leading causes of injury-related deaths among children. Injury-related deaths are well documented for SA, but this is not the case for non-fatal injuries.Objectives. To describe the non-fatal injuries sustained among children aged 0 - 13 years, to identify any significant sex differences by age group, cause of injury, admission status and injury severity.
 Methods. The trauma unit database from 1997 to 2016 at Red Cross War Memorial Children’s Hospital, Cape Town, was utilised for this analysis. The prevalence of injuries and the boy/girl ratios with 95% confidence intervals (CIs) were reported.Results. Analysis indicated significant differences by sex for individual injury causes (transport, assault, burns, falls and other injuries), age group, injury severity and admission status. Moderately severe injuries were largely caused by burns, while severe injuries were mostly transport related. Boys had significantly higher proportions of all injury causes. The boy/girl ratio was lowest for assault (1:18), where significantly more girls aged 1 - 3 and 4 - 6 years were injured. Rape/sexual assault was 5.5 times higher for girls, with a significantly higher proportion of moderate-severity injuries (87%; 95% CI 84.7 - 89.4).
 Conclusion. The study findings call for a more targeted prevention response for boy and girl children. Interventions should be targeted at the prevention of burns, traffic collisions and interpersonal violence, in particular sexual assaults against girls.
 
 
 

Effectiveness of Nautical Learning with Heuristic Strategy in Scientific Approach in terms of Critical Thinking Ability and Representation Flexibility

This study evaluates the effectiveness of implementing Heuristic Strategies within a Scientific Approach in nautical learning, focusing on Collision Prevention Regulations at Sea (P2TL). Despite the material's potential to enhance critical thinking skills and flexibility of representation, research applying Heuristic Strategies in the Scientific Approach still needs to be explored. Employing a quasi-experimental method with experimental and control classes, data collection involves written measurements. Research hypotheses are elucidated through inferential analysis, encompassing normality, homogeneity, and mean similarity tests on learning outcome scores. The results demonstrate that nautical learning with heuristic strategies in a scientific approach surpasses ordinary scientific approaches in fostering critical thinking skills and flexibility of representation. This research significantly contributes to advancing learning methodologies in nautics, particularly reinforcing students' critical thinking and representational flexibility. The implications provide educators with grounds to consider integrating Heuristic Strategies within Scientific Approaches to enhance nautical learning quality, aligning with evolving educational demands and student needs.

Spreading Awareness of Attenuating Vehicular Speed After Lifting of Covid-19 Lockdown using Web-Based Estimation of Visual Reaction Time for Primary Prevention of Road Traffic Collision

Objectives: Attenuating the vehicular speed after lifting of lockdown during Covid-19, by employing visual reaction time (RT) as a tool to prime citizens for creating decorum of driving and checking the road traffic fatalities. Background: It is indispensable to curb the driving speed post lockdown to avoid accidents. Even though, the impact of inactivity on RT has been well established, an insight into the new method can deal with the gross issue of road traffic casualty worldwide. Method: Using a web-based platform (http://physicsiology.com), RT was derived by visual stimulation test. Of which, the slowest and the fastest RT were obtained. Using these and prefeed average vehicular speed before lockdown in the website the post lockdown vehicular speed were quantified for the participant. Apart from spreading awareness the website has also collected data for age and days of lockdown. Results- Compared to pre lockdown vehicular speed, reduced post lockdown speed was well calculated and suggested. Furthermore, from 643 participants, there was a correlation between RT, age, and days of lockdown. Conclusion: Containment of speed can be achieved through RT to prime people for prevention of road traffic collision after lifting of Covid-19 lockdown.

Особливості розвитку відносин країн АСЕАН та перспективи співробітництва з Україною

Abstract. Introduction. Regional economic associations play an important role in the modern system of economic and political relations. One of them is the Association of Southeast Asian Nations, which aims to strengthen stable and peaceful relations in Southeast Asia and support the socio-economic development of member countries. Purpose. The purpose of the article is to analyze the development of ASEAN and identify the main promising areas of cooperation between ASEAN and Ukraine. Results. The article considers the main socio-economic indicators of the ASEAN countries in 2020, identifies the most important structural changes in exports and imports of the Association and their trade balance. The importance of intragroup exports and imports in 2010-2020 is analyzed. The geographical and commodity structure of trade is studied. Structural changes in export-import operations between ASEAN countries and Ukraine are considered. It was found that Ukraine provides ASEAN countries, to the greatest extent, with basic necessities, namely grain or cereals. Based on the obtained results, the forecast of the Association's trade volumes with Ukraine is built and the main prospects are highlighted. Conclusions. It is determined that in relations with ASEAN all three components are equally important for Ukraine: political, trade and economic and humanitarian. Therefore, the issues of joint counteraction to terrorism, cooperation in the field of cyber security, prevention of accidental collisions in the air and at sea should be actively considered. The priority in the economic sphere should be the export of agricultural products in the context of food security in Southeast Asia, cooperation in the fields of mechanical engineering, military-technical sphere, investment attraction. The Strategic Plan may consider the feasibility of establishing an FTA between Ukraine and ASEAN.

Research Review of Coordinate Measuring Machines

The fourth industrial revolution driven by digitalization, networking and intelligence has put forward new requirements for Coordinate Measuring Machines (CMMs). Two important development directions of Coordinate Measuring Machines are reviewed and analyzed in this paper. The first is the study of error compensation of Coordinate Measuring Machines, which includes the study of dynamic and static error and the study of dynamic error compensation technology. Dynamic error compensation is particularly important in high-speed measurement. And how to ensure measurement accuracy in high-speed measurement is an important direction for the development of Coordinate Measuring Machines. The second is about the intelligence of Coordinate Measuring Machines which includes CAD/CMM integration, independent decision-making, inspection planning, collision prevention and position recognition of the measured workpiece.

A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving.

The evaluation of take-over performance and take-over safety performance is critical to improving the take-over performance of conditionally automated driving, and few studies have attempted to evaluate take-over safety performance. This study applied a binary logistic model to construct a take-over safety performance evaluation model. A take-over driving simulator was established, and a take-over simulation experiment was carried out. In the experiment, data were collected from 15 participants who took over the vehicle and performed emergency evasive maneuvers while performing non-driving-related task (NDRT). Then, to calibrate the abnormal trajectory, the Kalman filter is adopted to filter the disturbed vehicle positioning data and the belief rule-based (BRB) method is proposed to warn irregular driving behavior. The results revealed that the accident rate of male participants is higher than that of female participants in the three frequency take-over experiment, and the overall driving performance of female participants is higher than that of male participants. Meanwhile, medium and high take-over frequencies have a significant effect on the prevention of vehicle collisions. In the take-over safety performance evaluation model, the minimum time to collision (TTC) of 2.3 s is taken as the boundary between the dangerous group and the safety group, and the model prediction accuracy rate is 87.7%. In sum, this study enriches existing research on the safety performance evaluation of conditionally automated driving take-over and provides important implications for the design of driving simulators and the performance and safety evaluation of human-machine take-over.

An RFID multi-Tag anti-collision algorithm based on Successive Interference Cancellation with power adaptive regulation

Radio Frequency Identification (RFID) is a key technology supporting the Internet of Things (IoT). The simultaneous identification of multiple Tag signals by a Reader will generate the Tag collision problem, which will significantly reduce the identification efficiency of the system and prolong the identification time in the scenario of large-scale Tags. In order to solve this problem, this paper researches RFID Tag collision prevention algorithm, and establishes a multi-Tag collision prevention algorithm model by analyzing the Aloha Tag collision prevention algorithm, which can accurately portray the slot efficiency and time efficiency of the system when recognizing multiple Tags; then establishes a power adaptive regulation RFID multi-Tag identification algorithm using Successive Interference Cancellation (SIC) technology. After simulation and verification results show that the constrained Reader can resolve the signal to noise ratio threshold of 6.9db, the slot efficiency and time efficiency of the SIC-based RFID Tag anti-collision algorithm is improved by 179% and 154% compared to the ISOC algorithm; the designed RFID power adaptive regulation algorithm is improved by 259% and 203% compared to the ISOC algorithm in terms of slot efficiency and time efficiency 203%.

크레인 충돌 방지를 위한 특징 추출 기반의 SLAM 기법

There is an increasing need for Simultaneous Localization and Mapping (SLAM) technology due to the recent demand for autonomous driving, metaverse, and augmented reality (AR) technologies. On industrial sites, demands for SLAM technologies that accurately estimate locations and generate precise maps of surrounding areas are becoming increasingly important for collision prevention between cranes. The research in this paper proposes a SLAM technology based on feature extraction techniques that create a precise map of the workspace in an industrial site by attaching a double-axis rotary Lidar to a tower crane. This configuration compensates for the limitation in single-axis rotary Lidar’s detection range. The double-axis rotary Lidar has a wider detection range than rotary Lidar, but the distribution of the collected data is irregular due to shaking and the difference in rotational speeds of the two motors in separate axes. SLAM techniques, including feature extraction and motion estimation suitable for these data characteristics, were introduced to aid in improving these data irregularities. Even when the SLAM algorithm operates accurately, the presence of afterimages from a dynamic object on the map can prevent effective collision performance and result in an inaccurate representation of the workspace. This paper explores an algorithm that effectively removes afterimages by scoring all the points collected by Lidar. The performance accuracy of the proposed SLAM method was evaluated by comparing the results of Mapping with those of conventional LOAM and G-ICP processes using data from attaching double-axis Lidar to the tower crane.

Ship-stopping algorithm utilizing VecTwin rudder system for automatic collision prevention

In this study, an algorithm for automatic ship stopping in an emergency is developed utilizing a VecTwin rudder system. Timing the emergency stop and determining the stopping capability of the ship equipped with the VecTwin rudder system are critical elements of the proposed stopping algorithm. The dangerous area of collision (DAC) between the ship and other ships is calculated to determine ship emergency stop. The algorithm is constructed such that the ship stops automatically after DAC, which is calculated based on the navigation situation between the own ship and the surrounding other ships, enters the emergency stopping region (ESR). The stopping capability of the subject ship equipped with the VecTwin rudder system was confirmed by a free-running model experiment with ship speeds determined by analyzing actual operational data. To validate the developed algorithm, two collision risk scenarios were selected—coastal navigation and in-port navigation—and simulations and free-running model tests were conducted for each scenario. It was demonstrated that the emergency stop algorithm developed for ships equipped with the VecTwin rudder system can reliably avoid collisions automatically. In addition, it was also confirmed that a shorter stopping distance was achieved than in the case of crash astern emergency stopping.

Exploring 15 years of brown bear (Ursus arctos)-vehicle collisions in northwestern Greece

Road networks provide several benefits to human societies; however, they are also one of the major drivers of fragmentation and habitat degradation. Their negative effects include wildlife-vehicle collisions which are associated with increased barrier effects, restricted gene flow, and increased local extinction risk. Large carnivores, such as the brown bear (Ursus arctos), are vulnerable to road mortality while they also put human safety at risk in every collision. We recorded approximately 100 bear-vehicle collisions during the last 15 years (2005–2020) in northwestern Greece and identified common aspects for collisions, i.e., spatial, or temporal segregation of collision events, road features, and age or sex of the involved animals. We recorded collisions in both the core distribution area of brown bears, as well as at the periphery, where few individuals, mostly males, disperse. According to our findings, there are four collision hotspots which include ca. 60% of total collisions. Bear-vehicle collisions occurred mostly in periods of increased animal mobility, under poor light conditions and low visibility. In most cases, we deem that a collision was unavoidable at the time of animal detection, because the driver could not have reacted in time to avoid it. Appropriate fencing, in combination with the retention of safe passages for the animals, can minimize collisions. Therefore, such mitigation measures, wildlife warning signs and other collision prevention systems, such as animal detection systems, should be adopted to decrease the number of bear-vehicle collisions and improve road safety.

A deep neural network approach for pedestrian trajectory prediction considering flow heterogeneity

Pedestrian trajectory prediction is imperative in specific fields, such as crowd management and collision prevention in automated driving environments. In this study, a novel long-short-term memory (LSTM)-based deep neural network capable of simulating the different walking behaviours of individuals with and without disabilities was designed. This network consists of three modules: the Disability module, the Environmental module, and the Trajectory Prediction module. Data from a large-scale pedestrian walking behaviour experiment involving individuals with disabilities were used to train and test the network. These data correspond to several experiments. Each experiment attempts to capture the essence of individuals’ walking behaviour in different situations. By sequencing and normalising the input data and applying regularisation techniques, the network was successfully trained. The results were compared to state-of-the-art models, demonstrating that the network can predict pedestrians’ trajectories more accurately, especially when pedestrian heterogeneity is involved.

Game‐based learning for better decision‐making: A collision prevention training for maritime transportation engineering students

AbstractShip collision is a form of maritime accident that possess great threats to transportation safety, economy, and human lives. Deck watchkeeping officers who oversee the safe navigation of the ships are responsible for preventing these collision accidents. Yet, most of the collisions are directly linked to the human errors onboard. One way to reduce these human errors is to train competent navigation officers for collision avoidance situations. However, training maritime students for complex collision situations is a challenging task. This study aims to examine if game‐based learning (GBL) can help students to perform better in collision avoidance situations by providing them the necessary skills to properly identify risks, goals, and cues in collision situations. With this purpose, we developed a digital game‐based decision‐making training containing eight collisions at sea scenarios. The developed tool was then presented to maritime engineering students as supplementary training. Thirty undergraduate maritime transportation engineering students participated in the study. A pretest/posttest control group design was adopted to investigate the effects of GBL. Tests are conducted with the use of a full mission bridge simulator. Observed changes in situational awareness, decision making, navigation, and collision avoidance performance of the participants were analyzed. The results showed that the intervention group had small but significant improvements in all dimensions compared to the control group. Overall, the game‐based decision‐making training helped students to reach a better understanding of the related course material. Required efforts and challenges to develop and incorporate such training into the existing maritime curriculum are discussed.

An Approach for Intersection Prevention in Transmission Channel Using Correlation Matrix and Tree Construction Model in V2X Framework

Typically, the delivery of information in vehicular ad hoc networks (VANETs) is regarded as a demanding one because of high mobility and invariable topological difference. One major concern in VANET framework is the occurrence of simulated intersection at which the packet reception probability become complicated for predicting and the model turn out to be complex highly. So as to overcome this dispute, a new framework is introduced which in turn prevents the intersection among the transmission path. Initially, the system model is initialized and the availability of channel is estimated. Based on the strength and size of data to be transmitted, the availability of channel is computed. After that, the best forwarding zone computation is made for minimizing the redundant data packets flow. To check the priority of packet and occurrence of packet collision, tree construction based data strength transmission is employed at last, the prevention of intersection or collision between the transmission channel is checked by means of correlation matrix based intersection prevention approach by monitoring the neighbor node. Then, the data packets were forwarded in an efficient manner without any intersection between the frames. The performance analysis is estimated in terms of network lifetime, packet delivery ratio, packet collision; inter node collision, throughput, and end-to-end delay.

A New Pattern for Detection of Streak-Like Space Target From Single Optical Images

In recent years, the continuously increasing number of space objects has caught great attention and triggered the need for space surveillance for the prevention of potential space collisions. Moving objects may appear as streaks in optical images in some situations. However, most of them are usually unknown so that they lack sufficient prior information, which brings great difficulty to detection. Motivated by the fact that streaks have their unique image characteristics, this article presents a novel detection pattern for streak-like targets in single optical images. First, a space target model from point-like target to streak-like target is studied to obtain characteristics. Next, the background removal method used in software named SExtractor is applied to find target candidates. Then, an operator is used to find local maximum points in each target candidate, and the points in the same candidate are clustered according to three domains including space, intensity, and distribution, to identify whether it contains a streak. Finally, the trajectory line of each cluster is extracted. The proposed method was tested on multiple sequences of collected real optical images, which contain targets of different features and found to obtain superior performance in the probability of detection, probability of false alarm, and receiver operating characteristic (ROC), in comparison to baseline methods. In particular, the setting of thresholds is intuitive because of the designed parameters with concrete meaning in images and the normalization process.

Vehicle to Pedestrian Systems: Survey, Challenges and Recent Trends

The accelerated rise of new technologies has reshaped the manufacturing industry of contemporary vehicles. Numerous technologies and applications have completely revolutionized the driving experience in terms of both safety and convenience. Although vehicles are now connected and equipped with a multitude of sensors and radars for collision avoidance, millions of people suffer serious accidents on the road, and unfortunately, the death rate is still on the rise. Collisions are still a dire reality for vehicles and pedestrians alike, which is why the improvement of collision prevention mechanisms is an ongoing necessity. Collision prevention mechanisms have evolved from vision-based systems like radars to systems that transcend the driver’s line of sight. These latter systems depend on vehicular ad hoc networks (VANETs) to employ bidirectional communication between vehicles and other vehicles (V2V) as well as between vehicles and road infrastructure (V2I). Recently, research has expanded to include a new communication system between vehicles and pedestrians (V2P) through the latter’s smartphones. In this paper, we provide an extensive survey of existing V2P projects, categorize different parameters that influence V2P system design, compare different communication technologies used in V2P systems and present an overview of recent trends that solve problems in V2P systems like network congestion, pedestrian localization, and context information exchange. The main contribution of our work is to pave the road for future research by providing a comprehensive view of projects, challenges and recent trends in the emerging and rapidly growing field of V2P system design.

Intelligent transport and technological means of a new generation and the underlying the concept of «digital autolift»

The article describes the concept of a «Digital Autolift», which is the basis of an intelligent transport and technological means (ITTS) designed for aircraft maintenance at the airport. The changes in the architecture and functionality of the autolift are analyzed with the introduction of digital technologies and the appearance of an on-board computer as the main tool for monitoring and managing ITTS systems. It describes what new transport and technological means may appear and how existing ones are transformed after digitalization. Collision prevention systems, control of technological operations, external communications system and proactive control and management system of ITTS are described.

Automated Roadside COW Animal Detection and Collision Prevention System

Automated Roadside COW Animal Detection and Collision Prevention System

Membrane-dependent relief of translation elongationarrest on pseudouridine- and N1-methyl-pseudouridine-modified mRNAs.

Expression of therapeutically important proteins has benefited dramatically from the advent of chemically modified mRNAs that feature decreased lability and immunogenicity. This had a momentous effect on the rapid development of COVID-19 mRNA vaccines. Incorporation of the naturally occurring pseudouridine (Ψ) or N1-methyl-pseudouridine (N1mΨ) into in vitro transcribed mRNAs prevents the activation of unwanted immune responses by blocking eIF2α phosphorylation, which inhibits translation. Here, we report that Ψs in luciferase (Luc) mRNA exacerbate translation pausing in nuclease-untreated rabbit reticulocyte lysate (uRRL) and promote the formation of high-order-ribosome structures. The major deceleration of elongation occurs at the Ψ-rich nucleotides 1294–1326 of Ψ-Luc mRNA and results in premature termination of translation. The impairment of translation is mainly due to the shortage of membranous components. Supplementing uRRL with canine microsomal membranes (CMMs) relaxes the impediments to ribosome movement, resolves collided ribosomes, and greatly enhances full-size luciferase production. CMMs also strongly stimulated an extremely inefficient translation of N1mΨ-Luc mRNA in uRRL. Evidence is presented that translational pausing can promote membrane recruitment of polysomes with nascent polypeptides that lack a signal sequence. Our results highlight an underappreciated role of membrane binding to polysomes in the prevention of ribosome collision and premature release of nascent polypeptides.

Dataset Evaluation for Multi Vehicle Detection using Vision Based Techniques

Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets.