Vehicle Collision Research Articles

Motor Vehicle Driving-Related Anxiety in Patients Undergoing Cardioverter Defibrillator Implantation and Cardiac Resynchronization Therapy With Defibrillators.

Reducing anxiety about motor vehicle driving in patients receiving implantable cardioverter defibrillators and cardiac resynchronization therapy with defibrillators is important not only for improving quality of life but also for preventing vehicle collisions owing to driver distraction. This study aimed to clarify the driving-related anxiety of patients with these defibrillators and the factors that predict such anxiety. We conducted a cross-sectional survey using a self-administered questionnaire of patients who had been driving a vehicle after device implantation at a general hospital between August 2018 and November 2019. The mean age was 60.8 ± 12.6 years. The reasons for implantation were primary prevention in 47 patients and secondary prevention in 30 patients. A total of 16 patients experienced anxiety about driving and 61 did not. Significantly more younger patients (mean age of 50.4 vs. 63.6 years, p < 0.001) and those with implantable cardioverter defibrillators had anxiety (100% vs. 73.8%, p = 0.02). Multivariable analysis indicated that age was the only independent factor that predicted driving-related anxiety (odds ratio, 0.937; 95% confidence interval, 0.883-0.993). Identifying and addressing driving-related anxiety in patients (particularly young patients) with defibrillators is important in preventing motor vehicle collisions and improving quality of life.

Traffic collisions and micromobility: A comparison between personal mobility devices and bicycles based on police reports

Introduction: The recent increase in the use of bicycles and personal mobility devices (PMDs), including mostly E-scooters, is associated with a rapid rise in injuries. Understanding the main crash scenarios leading to these injuries is essential to evaluate and improve preventive and protective measures, especially for PMDs, which are often equated with bicycles. The objective of this study is to identify and compare the most common two-party collision scenarios for bicycles and PMDs, and to identify factors affecting injury severity. Method: Crashes involving at least one PMD or one bicycle and another road user were analyzed from the 2019–2022 French police-reported road crashes database. We investigated the rider, the other vehicle, the road, and the crash scenarios characteristics (pre-crash maneuvers, impact zone on vehicles) and their joint effect on injury severity (hospitalization or fatality: yes/no). Results: We included 16,302 bicycle crashes and 4,118 PMD crashes in the analysis. Most of these collisions (75%) were against a car. The most frequent and the most severe collision scenario was the side-on-head for both bicycles (51%) and PMDs (58%); 67% of both bicycles and PMDs were going straight before the collision. Main factors associated with increased injury severity included colliding with a greater size vehicle, age above 50, and riding on roads with a higher speed limit. Bicycles remained at a higher risk of severe injury than PMDs after accounting for adjustment factors. Conclusions: Although collision scenarios appear similar for bicycles and PMDs, differences in other crash characteristics and injury severity suggest that these two modes of transportation should not be equated in crash investigations. Practical implications: These findings emphasize the need to primarily investigate side-on-head collisions with a moving car for both PMDs and bicycles in order to develop, evaluate, and improve protective devices to reduce the risk of injuries.

Stretchable and Self-Powered Mechanoluminescent Triboelectric Nanogenerator Fibers toward Wearable Amphibious Electro-Optical Sensor Textiles.

Flexible electro-optical dual-mode sensor fibers with capability of the perceiving and converting mechanical stimuli into digital-visual signals show good prospects in smart human-machine interaction interfaces. However, heavy mass, low stretchability, and lack of non-contact sensing function seriously impede their practical application in wearable electronics. To address these challenges, a stretchable and self-powered mechanoluminescent triboelectric nanogenerator fiber (MLTENGF) based on lightweight carbon nanotube fiber is successfully constructed. Taking advantage of their mechanoluminescent-triboelectric synergistic effect, the well-designed MLTENGF delivers an excellent enhancement electrical signal of 200% and an evident optical signal whether on land or underwater. More encouragingly, the MLTENGF device possesses outstanding stability with almost unchanged sensitivity after stretching for 200%. Furthermore, an extraordinary non-contact sensing capability with a detection distance of up to 35cm is achieved for the MLTENGF. As application demonstrations, MLTENGFs can be used for home security monitoring, intelligent zither, traffic vehicle collision avoidance, and underwater communication. Thus, this work accelerates the development of wearable electro-optical textile electronics for smart human-machine interaction interfaces.

An Autonomous Intelligent Liability Determination Method for Minor Accidents Based on Collision Detection and Large Language Models

With the rapid increase in the number of vehicles on the road, minor traffic accidents have become more frequent, contributing significantly to traffic congestion and disruptions. Traditional methods for determining responsibility in such accidents often require human intervention, leading to delays and inefficiencies. This study proposed a fully intelligent method for liability determination in minor accidents, utilizing collision detection and large language models. The approach integrated advanced vehicle recognition using the YOLOv8 algorithm coupled with a minimum mean square error filter for real-time target tracking. Additionally, an improved global optical flow estimation algorithm and support vector machines were employed to accurately detect traffic accidents. Key frames from accident scenes were extracted and analyzed using the GPT4-Vision-Preview model to determine liability. Simulation experiments demonstrated that the proposed method accurately and efficiently detected vehicle collisions, rapidly determined liability, and generated detailed accident reports. The method achieved the fully automated AI processing of minor traffic accidents without manual intervention, ensuring both objectivity and fairness.

Using animal–vehicle collision data for wildlife population monitoring

Using animal–vehicle collision data for wildlife population monitoring

Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles.

The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously construct the dynamic traffic scene representation in their brains, so this study first proposes the dynamic motion image representation method to reveal informative traffic situations in the motion-sensitive area (MSA), which provides a full view of surrounding cars. Then, this article develops a CNN model to extract the underlying features and learn driving policies from labeled datasets of MSA motion images. Besides, a safety-constrained layer is added to avoid vehicle collisions. We build a simulation platform based on the simulation of urban mobility (SUMO) to collect traffic datasets and test our proposed method. In addition, real-world traffic datasets are also involved to further investigate the proposed method's performance. The rule-based strategy and reinforcement learning (RL)-based method are used to compare with our approach. All results demonstrate that the proposed method performs lane-change decision-making much better than prevailing methods, which suggests our scheme has huge potential to accelerate the deployment of ACVs and is worth further study.

Comprehensive analysis of traumatic rupture of the aorta in road traffic crashes: incorporating epidemiological insights and K-prototype clustering

Objectives The identification of crash characteristics associated with traumatic rupture of the aorta (TRA) can significantly enhance countermeasures against TRA. Conventional epidemiological approaches struggle to adequately handle the substantial variability of traffic crash data. Consequently, this study aims to integrate conventional epidemiological analysis with data-driven cluster analysis to more comprehensively analyze TRA-related crash characteristics. Methods A total of 350 unweighted TRA crashes were extracted from traffic crash databases including comprehensive crash details and injury descriptions. Initially, a selection was made of 11 continuous variables and 9 categorical variables, describing crash characteristics. After correlation analysis and principal component analysis were applied to the dataset, K-prototype clustering was finally conducted using 6retained categorical variables and 6 principal components derived from the continuous variables. Results This study found significant age and gender disparities among TRA victims, with 50% falling within the age range of 25–59 years and an overwhelming majority (62.2%) being males. Side impacts emerged as the primary cause of TRA-related crashes (37.2%), followed by collisions with off-road objects (28.6%) and head-on collisions (24.8%). Cluster analyses revealed 6 distinct clusters within the TRA-related crash dataset. These clusters were characterized by factors such as vehicle model year, curb weight, collision dynamics, and seatbelt usage, providing a deeper understanding of the heterogeneity in TRA incidents and their associated factors. Conclusions Although limitations related to available data sources and factors such as accompanying injuries and vehicle weight warrant further comprehensive investigations in the future, this study contributes valuable insights into TRA analysis to enhance understanding and prevention strategies.

Epidemiological profile of polytrauma victims attended in a basic care hospital in the South of Brazil: retrospective cross-sectional study

Background: In the scope of public health, it is fundamental to comprehend the patterns and characteristics of the patients attended in hospitals for the guidance about prevention and intervention policies. In this perspective, the present study aims to evaluate the epidemiological profile of polytrauma victims who were assisted in a basic care hospital, located in the interior of Rio Grande do Sul, Brazil, during the period of January 1st to December 31th of 2022. The importance of studying the mechanism of trauma lies in the fact that it is the main cause of fatality in the population between 1 and 44 years old and represents a total of 5.8 million deaths annually in Brazil. Methods: The methodology consists of a retrospective cross-sectional study based on the analysis of hospital records, identifying patterns of gender, age, type of injury, modes of damage and outcome of the attended patients. Results: Based on the data collected, it was observed a predominance of the male gender among the polytrauma victims, with a significant incidence of assistance resulting from motor vehicle collisions, corroborating with previous epidemiological evidence. Conclusion: The epidemiological profile of polytraumatized patients is male, aged 41 and victims of traffic accidents. Therefore, urges the need to develop preventive and interventionist measures in relation to the causal factors of polytrauma, such as road infrastructure and alcohol use. In addition, the study was limited by the fact that it did not investigate the use of alcohol by the drivers, and the license status.

Geospatial analysis of injury severity on major roads in Ghana (2017–2020): implications for targeted injury prevention and control initiatives

BackgroundRoad safety authorities in high-income countries use geospatial motor vehicle collision data for planning hazard reduction and intervention targeting. However, low-income and middle-income countries (LMICs) rarely conduct such geospatial analyses...

Dynamic Performance Analysis of Precast Segment Column Reinforced with CFRP Subject to Vehicle Collision

With the extensive use of a precast segment column in the field of engineering, impact resistance performance has gradually attracted attention, as many accidents have caused huge economic losses and casualties. This study explored the dynamic response and failure modes of a prefabricated segment column both with and without Carbon Fiber-Reinforced Polymers (CFRPs). Firstly, numerical models of a precast segment column and CFRP-wrapped steel column under impact loads were developed, and the modeling method’s accuracy was fully verified. Then, numerical models of a bridge precast segment column with and without CFRPs under vehicle collision were established, and the differences in the dynamic performances between the precast segment column with and without CFRPs are explored. Finally, the effects of impact velocity, concrete strength, and CFRP thickness on the dynamic performance of a precast segment column are considered. The results indicate that in the case of a vehicle collision, multiple cross-sectional positions form highly complex stress states. At 100 km/h, the differences in bending moment and shear force values between reinforced and unreinforced precast segment columns at the impact section are 7.6% and 7.1%, respectively. At this velocity, the peak impact force also increases by 15.8% as the local stiffness of the precast segment column increases after reinforcement with a CFRP. The bottom segment of the precast segment column with a CFRP is crushed, and the precast segment column experiences shear failure under vehicle collision.

Limited cougar recolonization of eastern North America predicted by an individual-based model

As carnivores recolonize parts of their historical range, and such recolonization is increasingly recognized for its ability to reconfigure and revitalize degraded ecosystems, understanding where and when range expansion may continue is useful for facilitating scenario planning and identifying roles for conservation measures.We developed an individual-based model to predict carnivore range expansion and applied it to cougars (Puma concolor) in North America between 2023 and 2100. We parameterized our model with empirical movement, demographic, and survival data, and validated its performance by hindcasting nine observed recolonization events from the 1990s – 2023.Our model accurately recreated historical recolonization events and forecasted cougars reclaiming 2.1 % of unoccupied range by 2100, mostly in boreal Canada. Of currently unoccupied states/provinces (“jurisdictions”), only Manitoba received universal support across model runs for hosting a breeding population by 2100. Oklahoma, Minnesota, Kansas, and Iowa, requiring dispersal across nonhabitat, had 30 %, 30 %, 11 %, and 2 % probability of recolonization, respectively. No other jurisdictions were forecast to be recolonized.Mortality from harvest in Midwestern source populations and vehicle collisions dominated outcomes for eastward-moving females. Simulated management scenarios eliminating such hunting and adding nine wildlife crossing structures, however, did not significantly change recolonization probability.In areas where current cougar range abuts unoccupied breeding habitat, we estimated slower rates of expansion, varying from 2 to 3 km·yr−1, yielding roughly 150–230 km of linear expansion in those regions by 2100.Our mechanistic model and underlying empirical data provide a credible and transferable approach to forecast carnivore range expansion.

All-cause mortality and suicide after pediatric traumatic brain injury: a 20-year nationwide study in Finland

ObjectivesTo assess all-cause mortality and suicides after pediatric traumatic brain injury (pTBI). Study designWe conducted population-based historical cohort study using three nationwide registers from 1998 to 2018 in Finland. All patients that were the age of 0–17 at the time of the pTBI were included. The reference group consisted of children with ankle or wrist fractures. We used Kaplan–Meier and restricted mean survival time (RMST) analysis with 95% confidence intervals (CI) to compare all-cause mortality and suicides between groups. ResultsAfter 20 years of follow-up, there were 479 deaths in the pTBI group (0.67% of 71,963) and 306 deaths in the reference group (0.47% of 64,848). In the pTBI group, 28.6% of the deaths occurred after the first follow-up year, compared to 2.6% in the reference group. In all-cause mortality, survival time was slightly less in the pTBI group with age and gender adjustment throughout the follow-up period [20-year RMST ratio: 0.995; CI (0.994–0.996)]. The leading manners of death were suicides (pTBI group = 28.4%; reference group = 45.5%) and traffic collisions (pTBI group = 37.4%; reference group = 20.8%). Age and gender-adjusted survival time was slightly less for those with suicide as a manner of death in the pTBI group [10-year RMST ratio: 0.999; CI (0.999–0.999); 20-year RMST ratio: 0.999; CI (0.998–0.999)]. ConclusionsChildren and adolescents who sustained a TBI have slightly lower long-term survival time for all-cause mortality, most of which occurs during the first year following injury. There is no clinically meaningful difference in deaths by suicide between the two injury groups.

The impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania.

The national lockdowns that occurred all over the world in response to the Coronavirus Disease 2019 (COVID-19) pandemic have been found to have impacted alcohol use. The aim was to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities in Lithuania. Using monthly data from the Lithuanian Road Police Service for January 2004 to December 2022, we performed interrupted time-series analyses using a generalized additive model to evaluate the impact of COVID-19-related national lockdowns on alcohol-related traffic collisions, injuries, and fatalities. In Lithuania, the COVID-19-related lockdowns occurred from March 2020 to June 2020 and from November 2020 to June 2021. Although overall rates for traffic collisions and injuries decreased during the COVID-19-related lockdowns in Lithuania, these lockdowns were associated with a 3.21% (95% CI: 1.19%, 5.23%) increase in the relative proportion of alcohol-related traffic collisions and a 2.46% (95% CI: 0.12%, 4.80%) increase in the relative proportion of alcohol-related traffic injuries. The association between the lockdowns and alcohol-related traffic fatalities was not statistically significant. The COVID-19-related national lockdowns in Lithuania were associated with a decrease in the overall rate of traffic collisions and injuries, but an increase in the relative proportion of alcohol-related traffic collisions and injuries.

USV Collision Avoidance Decision-Making Based on the Improved PPO Algorithm in Restricted Waters

The study presents an optimized Unmanned Surface Vehicle (USV) collision avoidance decision-making strategy in restricted waters based on the improved Proximal Policy Optimization (PPO) algorithm. This approach effectively integrates the ship domain, the action area of restricted waters, and the International Regulations for Preventing Collisions at Sea (COLREGs), while constructing an autonomous decision-making system. A novel set of reward functions are devised to incentivize USVs to strictly adhere to COLREGs during autonomous decision-making. Also, to enhance convergence performance, this study incorporates the Gated Recurrent Unit (GRU), which is demonstrated to significantly improve algorithmic efficacy compared to both the Long Short-Term Memory (LSTM) network and traditional fully connected network structures. Finally, extensive testing in various constrained environments, such as narrow channels and complex waters with multiple ships, validates the effectiveness and reliability of the proposed strategy.

Lack of Association between Cervical Spine Injuries and Prehospital Immobilization: From Tradition to Evidence.

Background: Cervical spine (C-spine) trauma usually results from blunt injuries and is traditionally managed by prehospital spinal immobilization using a cervical collar. We sought to examine if prehospital C-spine immobilization is associated with actual C-spine injuries and what factors are associated with the decision to immobilize the C-spine. Methods: We retrospectively analyzed blunt trauma patients treated by Israeli Defense Force (IDF) medical teams from 2015 to 2020. Children, penetrating injuries, and non-threatening injuries were excluded. Demographic data, injury characteristics, and prehospital information were collected from the IDF Trauma Registry's electronic medical records and merged with corresponding hospital data from the Israeli National Trauma Registry. Results: Overall, 220 patients were included, with a mean age of 32 and a predominance of male patients (78%). Most injuries were due to motor vehicle collisions (77%). In total, 40% of the patients received a cervical collar. C-spine injuries were present in 8%, of which 50% were immobilized with a cervical collar. There were no significant differences in the incidences of C-spine injuries or disability outcomes with or without collar immobilization. The use of a collar was significantly associated with backboard immobilization (OR = 14.5, p < 0.001) and oxygen use (OR = 2.5, p = 0.032). Conclusions: Prehospital C-spine immobilization was not associated with C-spine injury or neurological disability incidences. C-spine immobilization by medical providers may be influenced by factors other than the suspected presence of a C-spine injury, such as the use of a backboard. Clear clinical guidelines for inexperienced medical providers are called for.

Urbanisation has impacted the population genetic structure of the Eurasian red squirrel in Japan within a short period of 30 years

Human activities often induce maladaptive effects in urban wildlife, such as increased inbreeding and decreased gene flow, consequently leading to a loss of biodiversity. Although maladaptive effects are commonly reported in urban wildlife, some wildlife species are not affected. Understanding the conditions and factors that affect the genetic structure of wildlife is crucial for its conservation in urban environments. Eurasian red squirrels Sciurus vulgaris in Obihiro City, Hokkaido, Japan, have settled in large residential lots with a high risk of vehicle collisions for approximately 30 years, raising the possibility that the urban population would be genetically isolated from nearby rural populations and may experience a rapid decrease in genetic diversity. In this study, we analysed the population structure, genetic distance, and nucleotide diversity of squirrels in the Obihiro area using genome-wide single nucleotide polymorphisms (SNPs). Principal component analysis and ADMIXTURE showed that urban and rural squirrel populations formed distinct clusters with intermediate urban and rural boundary populations. Genetic distances between the urban populations were greater than that of other pairs. Nucleotide diversities were lower in urban populations than in other populations, suggesting limited gene flow between urban and other populations, possibly due to urbanisation. The change in the genetic characteristics of urban squirrel populations during this short period demonstrates the strong impact of urbanisation. Our findings suggest that a genome-wide approach to the genetic structure of wildlife contributes to its conservation in urban environments.

Predictors of mortality for blunt trauma patients in intensive care: A retrospective cohort study

Background: Major trauma places substantial demand on critical care services, is a leading cause of death in under 40-year-olds and causes significant morbidity and mortality across all age groups. Various factors influence patient outcome and predefining these could allow prognostication. The aim of this study was to identify predictors of mortality from major trauma in intensive care. Methods: This was a retrospective study of adult trauma patients admitted to general intensive care between January 2018 and December 2019. We assessed the impact on mortality of patient demographics, patterns of injury, injury scores (Glasgow Coma Score (GCS), Charlson’s comorbidity index (CCI), Acute Physiology and Health Evaluation II (APACHE II), Injury Severity Score (ISS) and Probability of Survival Score (Ps19)), number of surgeries and mechanism of injury using logistic regression. Results: A total of 414 patients were included with a median age of 54 years (IQR 34–72). Overall mortality was 18.6%. The most common mechanism of injury was traffic collision (46%). Non-survivors were older, had higher ISS scores with lower GCS on admission and probability of survival scores. Factors independently predictive of mortality were increasing age (OR 1.06, p &lt;0.001) and GCS &lt;15 on admission (OR 7.21, p &lt;0.001). Ps19 was the best predictor of mortality (p &lt;0.001 for each score category), with an AUROC of 0.90. Conclusions: The significant mortality predictors were age, fall from &lt;2 metres, injury of head or limbs, GCS &lt;15 and Ps19. Contrary to previous studies CCI and APACHE II did not significantly predict mortality. Although Ps19 was found to be the best current prognostic score, trauma prognostication would benefit from a single validated scoring system incorporating both physiological variables and injury patterns.

Research on occupant injury severity prediction of autonomous vehicles based on transfer learning

AbstractThe focus of the future of autonomous vehicles has shifted from feasibility to safety and comfort. The seat of an autonomous vehicle may be equipped with a rotational function, and the occupant's sitting position would be diverse. This poses a higher challenge to occupant injury protection during vehicle collisions. The main objective of the current study is to develop occupant injury prediction models for autonomous vehicles that can be used to predict the injury severity of occupants in different seat orientations and sitting positions. The first step is to establish an occupant crash model database with different seat orientations. It is used to simulate the occupant crash injury database of an autonomous vehicle, considering seat rotation and the back inclination angle. The second step is to establish a pre‐training occupant injury prediction model based on the existing database and then train the autonomous vehicle occupant injury prediction model using an in‐house database based on the transfer learning method. Occupant injury prediction models achieve good accuracy (82.8% on the numerical database and 62.9% on the real verification database) and shorter computational time (4.86 ± 0.33 ms) on the prediction tasks. Finally, the influence of the model input variables is analyzed. This study demonstrates the feasibility of using a small‐sample database based on transfer learning for occupant injury prediction in autonomous vehicles.

Impact of acute stress disorder on surfactant protein D levels in acute lung injury.

Many people sustain acute lung injuries in road traffic collisions, but few studies have dealt with such injuries in live models. This study aimed to explore the basic pathophysiological and inflammatory changes in adult rabbits following acute thoracic trauma. We randomly assigned 50 rabbits to control and injury groups. Rabbits in the injury group were subjected to right chest pressure (2600g) using a Hopkinson bar. Measurements were taken in the control group and 0, 24, 48, and 72h after injury in the injury group. Injury severity was evaluated in gross view; with haematoxylin and eosin (H&E) staining; and through the serum changes of tumor necrosis factor alpha (TNF-α), surfactant protein D (SP-D), and neutrophils. Secretion changes in SP-D in right lung injured tissues were estimated by western blotting and qPCR. Serum TNF-α levels increased rapidly immediately after injury, gradually recovering after 24, 48, and 72h (p < 0.01). The percentage of neutrophils in the accompanying blood showed a consistent trend. Gross necropsy and H&E staining indicated different levels of bleeding, alveoli exudation, and inflammatory transformation after impact. ELISA depicted the same trend in circulation (F = 22.902, p < 0.01). Western blotting showed that SP-D protein levels in tissues decreased at 0h and increased at 24, 48, and 72h. We demonstrate the feasibility of a model of impact lung injury. Primary impact caused injury without external signs. Inflammation began immediately, and the lungs began recovering at 24, 48, and 72h, as shown by increased SP-D levels in circulation and tissues.With complaints of ALI and inflammation, SP-D may be a potential biomarker after chest trauma.

The effect of instance mask-guided attention for discriminative learning of vehicular collision image classification

The effect of instance mask-guided attention for discriminative learning of vehicular collision image classification