Search Results (374)

Internal combustion engine vehicles damage the environment and public health by emitting toxic fumes, such as CO₂ or CO and other trace compounds. The use of electric cars helps to reduce the emission of pollutants into the environment due to the use of batteries with no direct and local emissions. However, accidents of battery electric vehicles pose new challenges, such as thermal runaway. Such accidents can be serious and, in some cases, may result in uncontrolled overheating that causes the battery pack to spontaneously ignite. In particular, the most dangerous vehicles are heavy goods vehicles (HGVs), as they release a large amount of energy that generate high temperatures, poor visibility, and respiratory damage. This study aims to determine the potential consequences of large BEV fires in road tunnels using computational fluid dynamics (CFD). Furthermore, a comparison between a BEV and an ICEV fire shows the differences related to the thermal and the toxic impact. Furthermore, the adoption of a longitudinal ventilation system in the tunnel helped to mitigate the BEV fire risk, keeping a safer environment for tunnel users and rescue services through adequate smoke control. Full article

We have improved the efficiency of the protection of occupants of cars by effectively reducing the injury and mortality rate caused by accidents when using safety belts. To ensure the protection efficiency of the safety belt outlet cover, we tested and adjusted the following parameters: the filling time, flow-front temperature and switching pressure, injection position pressure, locking force, shear rate, shear force, air hole, melting mark, material flow freezing-layer factor, volume shrinkage rate during jacking out, coolant temperature and flow rate in the cooling stage, part temperature, mold temperature difference, deflection stage, warping deformation analysis, differential cooling, differential shrinkage, and directional effect. Full article

(1) Background: The number of casualties due to car accidents in Europe is decreasing. However, there are still very large differences in the levels of road safety between countries, even within the European Union. Therefore, it is vital to conduct reliable international analyses to compare the effectiveness of actions taken to prevent road accidents. Information on the number of accidents, injuries, and fatalities can be found in various databases (e.g., Eurostat or OECD). In this paper, it is clearly shown that data on car accidents and the resulting injuries are not comparable between different countries, and any conclusions drawn using these data as their basis will be erroneous. (2) Methods: The indicators of the number of car accidents, injured people, and fatalities in relation to the number of inhabitants were determined, then their distribution and mutual correlations were examined for a group of selected European countries. (3) Results: There is no correlation between the indicators of the number of car accidents and injuries and the indicator of fatalities. An assessment of road safety based on these indicators would result in inconsistent and ambiguous conclusions. (4) Conclusions: It has been empirically shown that data on the number of car accidents and injured people from different countries are not comparable. These conclusions were verified by providing examples of the definitions of an injured person used in different countries. This paper clearly indicates that any international comparisons can only be made based on data regarding the number of road accident fatalities. Full article

Road traffic accidents pose a substantial global public health burden, resulting in significant fatalities and economic costs. This study employs Bayesian Poisson regression to model traffic accident rates in Poland, focusing on the intricate relationships between weather conditions and socioeconomic factors. Analyzing both yearly county-level and weekly nationwide data from 2020 to 2023, we created four distinct models examining the relationships between accident occurrence and predictors including temperature, humidity, precipitation, population density, passenger car registrations, and road infrastructure. Model evaluation, based on WAIC and PSIS-LOO criteria, demonstrated that integrating both weather and socioeconomic variables enhanced predictive accuracy. Results showed that socioeconomic variables—especially passenger car registrations—were strong predictors of accident rates over longer timeframes and across localized regions. In contrast, weather variables, particularly temperature and humidity, were more influential in explaining short-term fluctuations in nationwide accident counts. These findings provide a statistical foundation for identifying high-risk conditions and guiding targeted interventions. The study supports Poland’s national road safety goals by offering evidence-based strategies to reduce accident-related fatalities and injuries. Full article

The frequency of road accidents in specific locations is determined by a number of variables, among which an important role is played not only by common determinants such as inappropriate behavior of road users, but also by external factors characterizing a given location. Taking this into account, the main objective of the study was to answer the question of which variables determine that the intensity of car accidents is higher in certain parts of the city of Poznań compared to other locations. The study was based on source data from the police Accident and Collision Records System (SEWiK). For the purposes of the analysis, two variants of the regression method were used: ordinary least squares (OLS) and geographically weighted regression (GWR). The obtained results made it possible to identify variables that increase the likelihood of a traffic accident in specific parts of the city, and the variables that proved to be statistically significant include the size of the built-up area and the number of traffic lights. The results obtained using the GWR technique indicate that the way in which the analyzed features influence road accidents can vary across the city, which may emphasize the complexity of the analyzed phenomenon. The results can be used by relevant entities (transport traffic planners and many others) to create road safety policies. Full article

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, wasted time, and resources. However, remote driver intervention may be necessary in extreme situations to ensure safe roadside parking or complete remote takeover. In these cases, high-quality real-time video streaming is crucial for remote driving. In a preliminary study, we presented a region of interest (ROI) High-Efficiency Video Coding (HEVC) method where the image was segmented into two categories: ROI and background. This involved allocating more bandwidth to the ROI, which yielded an improvement in the visibility of classes essential for driving while transmitting the background at a lower quality. However, migrating the bandwidth to the large ROI portion of the image did not substantially improve the quality of traffic signs and lights. This study proposes a method that categorizes ROIs into three tiers: background, weak ROI, and strong ROI. To evaluate this approach, we utilized a photo-realistic driving scenario database created with the Cognata self-driving car simulation platform. We used semantic segmentation to categorize the compression quality of a Coding Tree Unit (CTU) according to its pixel classes. A background CTU contains only sky, trees, vegetation, or building classes. Essentials for remote driving include classes such as pedestrians, road marks, and cars. Difficult-to-recognize classes, such as traffic signs (especially textual ones) and traffic lights, are categorized as a strong ROI. We applied thresholds to determine whether the number of pixels in a CTU of a particular category was sufficient to classify it as a strong or weak ROI and then allocated bandwidth accordingly. Our results demonstrate that this multi-category ROI compression method significantly enhances the perceptual quality of traffic signs (especially textual ones) and traffic lights by up to 5.5 dB compared to a simpler two-category (background/foreground) partition. This improvement in critical areas is achieved by reducing the fidelity of less critical background elements, while the visual quality of other essential driving-related classes (weak ROI) is at least maintained. Full article

Road traffic accidents (RTAs) are a leading cause of physical injury worldwide, but they also frequently result in post-traumatic stress disorder (PTSD). This systematic review examines the prevalence, predictors, comorbidity, and treatment of PTSD among RTA survivors. Four electronic databases (PubMed, Scopus, EBSCO, and ProQuest) were searched following PRISMA 2020 guidelines. Articles were included if reporting on the presence of post-traumatic stress disorder as a result of a road traffic accident in adults aged 18 years and older. Including peer-reviewed journal articles and awarded doctoral theses across all publication years, and written in English, Macedonian, Serbian, Bosnian, Croatian, and Bulgarian, identified 259 articles, and using Literature Evaluation and Grading of Evidence (LEGEND) assessment of evidence 96 were included in the final review, involving 50,275 participants. Due to the heterogeneity of findings, quantitative data were synthesized thematically rather than through meta-analytic techniques. Findings are reported from Random Control Trial (RCT) and non-RCT studies. PTSD prevalence following RTAs ranged widely across studies, from 20% (using Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-5 criteria) to over 45% (using International Classification of Diseases, 10th Revision, ICD-10 criteria) within six weeks post-accident (non-RCT). One-year prevalence rates ranged from 17.9% to 29.8%, with persistence of PTSD symptoms found in more than half of those initially diagnosed up to three years post-RTA (non-RCTs). Mild or severe PTSD symptoms were reported by 40% of survivors one month after the event, and comorbid depression and anxiety were also frequently observed (non-RCTs). The review found that nearly half of RTA survivors experience PTSD within six weeks, with recovery occurring over 1 to 3 years (non-RCTs). Even minor traffic accidents lead to significant psychological impacts, with 25% of survivors avoiding vehicle use for up to four months (non-RCT). Evidence-supported treatments identified include Cognitive Behavioural Therapy (CBT) (RCTs and non-RCTs), Virtual Reality (VR) treatment (RCTs and non-RCTs), and Memory Flexibility training (Mem-Flex) (pilot RCT), all of which demonstrated statistically significant reductions in PTSD symptoms across validated scales. There is evidence for policy actions including mandatory and regular psychological screening post RTAs using improved assessment tools, sharing health data to better align early and ongoing treatment with additional funding and access, and support and interventions for the family for RTA comorbidities. The findings underscore the importance of prioritizing research on the psychological impacts of RTAs, particularly in regions with high incident rates, to understand better and address the global burden of post-accident trauma. Full article

The automatic identification of traffic collisions is an emerging topic in modern traffic surveillance systems. The increasing number of surveillance cameras at urban intersections connected to traffic surveillance systems has created new opportunities for leveraging computer vision techniques for automatic collision detection. This study investigates the effectiveness of transfer learning utilizing pre-trained deep learning models for collision detection through dashcam images. We evaluated several state-of-the-art (SOTA) image classification models and fine-tuned them using different hyperparameter combinations to test their performance on the car collision detection problem. Our methodology systematically investigates the influence of optimizers, loss functions, schedulers, and learning rates on model generalization. A comprehensive analysis is conducted using 7 performance metrics to assess classification performance. Experiments on a large dashcam-based images dataset show that ResNet50, optimized with AdamW, a learning rate of 0.0001, CosineAnnealingLR scheduler, and Focal Loss, emerged as the top performer, achieving an accuracy of 0.9782, F1-score of 0.9617, and IoU of 0.9262, indicating a strong ability to reduce false negatives. Full article

Whiplash-associated disorder (WAD) is common following motor vehicle collisions (MVCs). The yearly incidence rate in the Western world has been reported to be around 300 per 100,000 habitants, but no publications have examined yearly incidence across a period that includes the COVID-19 pandemic. A retrospective, epidemiological study was conducted in Iceland involving data from the University Hospital and the healthcare centers for the Capital Region for all individuals who visited the emergency department during 2010–2024 due to TAs, with a diagnosis indicating whiplash injury. The yearly incidence rate was calculated and presented per 100,000 person-years and analyzed by age, sex, months, and weekdays. The overall incidence of whiplash injuries was 267 per 100,000 person-years, greater for females than males (p < 0.001) with a significant effect of age (p < 0.001), the greatest rate being seen in young adulthood. A significant effect of time was seen across the study period (p < 0.001) due to a sharp decline between 2016 and 2020, followed by a continued low yearly incidence rate, with the smallest one seen in 2024 (78 per 100,000). Despite an increase in MVCs worldwide, the incidence of whiplash injuries following MVCs has declined significantly over the past decade. This trend may reflect shifts in injury patterns, healthcare-seeking behavior, or reporting practices. Full article

Unobtrusive in-vehicle measurement and the monitoring of physiological signals have recently attracted researchers in industry and academia as an innovative approach that can provide valuable information about drivers’ health and status. The main goal is to reduce the number of traffic accidents caused by driver errors by monitoring various physiological parameters and devising appropriate actions to alert the driver or to take control of the vehicle. The research on this topic is in its early stages. While there have been several publications on this topic and industrial prototypes made by car manufacturers, a comprehensive and critical review of the current trends and future directions is missing. This review examines the current research and findings in in-vehicle physiological monitoring and suggests future directions and potential uses. Various physiological sensors, their potential locations, and the results they produce are demonstrated. The main challenges of in-vehicle biosensing, including unobtrusive sensing, vehicle vibration and driver movement cancellation, and privacy management, are discussed, and possible solutions are presented. The paper also reviews the current in-vehicle biosensing prototypes built by car manufacturers and other researchers. The reviewed methods and presented directions provide valuable insights into robust and accurate biosensing within vehicles for researchers in the field. Full article

This study uses a public health approach to identify the comorbid risk factors and protective factors that influence the likelihood of an officer-involved shooting (OIS). Methods: We analyzed 7.5 years of hospital inpatient data obtained from the state of Texas. The OIS subjects (n = 177) were civilians who were shot during a legal intervention involving law enforcement. The control group (n = 33,539) included persons who were hospitalized due to injuries from a car accident. Logistic regression models were used to identify the predictors of an OIS incident. The data included information on chronic diseases, vulnerable population status, health insurance, mental health diagnoses, substance use disorders, and recent trauma. Results: About one-fourth (24.3%) of OIS subjects had a diagnosed mental illness, compared to 8.4% of control subjects (p < 0.001). Factors that greatly increased the risk for an OIS included the following: schizophrenia (AOR = 2.7; CI: 1.6, 4.6), methamphetamine use disorder (AOR = 3.5; CI: 2.2, 5.5), and recent family bereavement (AOR = 8.5; CI: 1.8, 39.6). Six subjects (3.4%) were persons experiencing homelessness (PEH). Protective factors that lowered the risk for an OIS included commercial health insurance (AOR = 0.27; CI: 0.17, 0.45) and Medicaid insurance (AOR = 0.61; CI: 0.11, 0.93). Conclusions: These findings underscore the preventable nature of many OIS incidents, especially those that involve untreated mental illness, homelessness, substance use disorders, and recent trauma. Addressing the root causes of these incidents will likely require interdisciplinary collaboration among law enforcement, public health agencies, and social services. Full article

During the continuous casting process, the submerged entry nozzle (SEN) should be maintained at the geometric center of the mold. However, in actual production, factors such as deformation of the tundish bottom and inaccurate positioning of the traversing car occasionally cause SEN offset. SEN offset can make the molten steel flow field in the mold asymmetric, increasing the risks of slag entrainment on the surface of the casting blank and breakout accidents. To evaluate the influence of different SEN offsets on the mold flow field, this study uses a slab continuous casting mold with a cross-section of 920 mm × 200 mm from a specific factory as the research object. Mathematical simulations were used to investigate the influence of SEN offsets (including width-direction and thickness-direction offsets) on the flow behavior of molten steel in the mold. A physical water model at a 1:1 scale was established for verification. Two parameters, the symmetry index (S) and the bias flow index (N), were introduced to quantitatively evaluate the symmetry of the flow field, and the rationality of the liquid-level fluctuation under this flow field was verified using the F-number (proposed by Japanese experts for mold level fluctuation control) from the index model. The results show the following: when the SEN offset in the thickness direction increases from 0 to 50 mm, the longitudinal symmetry index (Sy) of the molten steel flow field in the mold decreases from 0.969 to 0.704—a reduction of 27.4%; the longitudinal bias flow index (Ny) of molten steel level fluctuation increases from 0.007 to 0.186, representing a 25.6-fold increase, and the F-number rises from 4.297 to 8.482; when the SEN offset in the width direction increases from 0 to 20 mm, the transverse-axis symmetry index (Sx) of the flow field decreases gradually from 0.969 to 0.753 at a 20 mm offset, which is a reduction of approximately 22.29%; the transverse-axis bias flow index (Nx) increases from 0.015 to 0.174 at a 20 mm offset—an increase of 10.6 times; and the F-number increases from 4.297 to 5.548. Considering the comprehensive evaluation of horizontal/vertical symmetry indices, bias flow indices, and F-numbers under the two working conditions, the width-direction SEN offset has the most significant impact on the symmetry of the molten steel flow field. Full article

Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety. Full article

This study analyzes factors influencing injury severity in motorcycle accidents involving non-helmeted riders using Bayesian spatiotemporal logistic models. Five models were developed, four of which incorporated different spatiotemporal configurations, including spatial, temporal, and spatiotemporal interaction error terms. The results indicate that the optimal model integrated Leroux CAR spatial priors, temporal random walks, and interaction terms, achieving 86.74% classification accuracy, with a 3% reduction in the DIC value; obtaining the lowest numerical fit demonstrating spatiotemporal interactions is critical for capturing complex risk patterns (e.g., rain amplifying nighttime collision severity). The results highlight rain (OR = 1.53), age ≥ 50 (OR = 1.90), and bi-directional roads (OR = 1.82) as critical risk factors. Based on these findings, several sustainable traffic safety strategies are proposed. Short-term measures include IoT-based dynamic speed control on high-risk roads and app-enforced helmet checks via ride-hailing platforms. Long-term strategies integrate age-specific behavioral training focusing on hazard perception and reaction time improvement, which reduced elderly fatalities by 18% in Japan’s “Silver Rider” program by directly modifying high-risk riding habits (non-helmets). These solutions, validated by global case studies, demonstrate that helmet use could mitigate over 60% of severe head injuries in these high-risk scenarios, promoting sustainable traffic governance through spatiotemporal risk targeting and helmet enforcement. Full article

Efficient pedestrian detection plays an important role in many practical daily life applications, such as autonomous cars, video surveillance, and intelligent driving assistance systems. The main goal of pedestrian detection systems, especially in vehicles, is to prevent accidents. By recognizing pedestrians in real time, these systems can alert drivers or even autonomously apply brakes, minimizing the possibility of collisions. However, occlusion is a major obstacle to pedestrian detection. Pedestrians are typically occluded by trees, street poles, cars, and other pedestrians. State-of-the-art detection methods are based on fully visible or little-occluded pedestrians; hence, their performance declines with increasing occlusion level. To meet this challenge, a pedestrian detector capable of handling occlusion is preferred. To increase the detection accuracy for occluded pedestrians, we propose a new method called the Discriminative Deformable Part Model (DDPM), which uses the concept of breaking human image into deformable parts via machine learning. In existing works, human image breaking into deformable parts has been performed by human intuition. In our novel approach, machine learning is used for deformable objects such as humans, combining the benefits and removing the drawbacks of the previous works. We also propose a new pedestrian dataset based on Eastern clothes to accommodate the detector’s evaluation under different intra-class variations of pedestrians. The proposed method achieves a higher detection accuracy on Pascal VOC and VisDrone Detection datasets when compared with other popular detection methods. Full article