In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and ...sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.
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The aim of this research is to study the specifics of the road accident rate formation processes in regions of the Russian Federation (2021) using information-entropic analysis. The typical research ...approaches (correlation-regression, factorial analyses, simulation modelling, etc.) do not always allow us to identify its specificity. It is impossible to evaluate the quality of the researched process’s structure using these methods. However, this knowledge is required to understand the distinctions between high-quality road safety management and its opposite. In order to achieve the goal of the research methodology based on the use of the classical approaches of C. Shannon, the quantitative value of information entropy H was elaborated. The key components of this method are the modelling of the cause-and-effect chain of road accident rate formation and the consideration of the relative significances of individual blocks of the process in achieving the final result. During the research the required statistical data were collected and the structure of the road accident rate formation process in 82 regions of the Russian Federation in the format “Population P—Fleet of vehicles NVh—Road Traffic Accidents NRA—RTA Victims NV—Fatality Cases ND” was analyzed. The fact that the structure of the road accident rate formation process is extremely specific in different Russian regions was shown. Exactly this specificity forms the degree of ambiguity in the state of Russian regional road safety provision systems in terms of the probability of death in road accidents. The main conclusion of this research is that information-entropic analysis can be successfully used to assess the structural quality of road safety systems.
An analysis of the national traffic collision trends in Japan for the January 2018 to June 2022 period using existing statistical data indicates that the number of traffic incidents, injuries, and ...fatalities decreased over time. After the outbreak of COVID-19 in December 2019, traffic volume decreased. In this study, to explore how the COVID-19 pandemic correlates with traffic collisions, we used the Spearman rank correlation of non-parametric statistical test to compare the number of COVID-19 infections with the number of traffic collisions. The number of COVID-19 infections showed a significant inverse correlation with the number of traffic collisions nationwide, in some regions, and in some prefectures. When the number of COVID-19 infections increased, a State of Emergency or Semi-Emergency Spread Prevention Measures were repeatedly declared. We submit that these measures along with the restrictions on the population's autonomy and movement to prevent the spread of infection, reduces the number of traffic incidents, injuries, and fatalities owing to a decrease in traffic volume. Therefore, these lessons learned from the COVID-19 pandemic advocate that regulation of vehicle traffic volume is an effective means of reducing the occurrence of traffic collisions. These results can be applied to future policy development to support road safety improvements during unique events.
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•We predict the transition from non-fatigue to fatigue using physiological features.•We capitalize on PERCLOS as the ground truth of driver fatigue.•We select most critical physiological features to ...predict driver fatigue proactively.•We predict the fatigue transition using nonlinear autoregressive exogenous network.•The accuracy of fatigue transition prediction evidences the potential of our method.
One of the main causes of traffic accidents is driver fatigue due to monotonous driving, sleep deprivation, boredom, or a combination of these. Thus, fatigue detection systems have been proposed to alert drivers. However, how early driver fatigue can be detected often determines the effectiveness of the system. Traditional approaches aim to detect driver fatigue in real time, which can be too late in many critical situations, such as the takeover transition period in highly automated driving. Therefore, in this research, we aim to predict the driver's transition from non-fatigue to fatigue in highly automated driving using physiological features. First, we capitalized on PERCLOS (i.e., PERcent of time the eyelids CLOSure) as the ground truth of driver fatigue. Next, we selected the most important physiological features to predict driver fatigue proactively. Finally, using these critical physiological features, we built prediction models that were able to predict the fatigue transition at least 13.8 s ahead of time using a technique called nonlinear autoregressive exogenous network. The accuracy of fatigue transition prediction was promising for highly automated driving (F1 measure = 97.4% and 99.1% for two types of models), which demonstrated the potential of the proposed method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Statistical surveys show that the majority of traffic accidents occur due to low visibility, highlighting the need to delve into innovative car lighting technologies. A car driver must not only be ...able to see but also to be seen. The issue of headlight illumination is vital, especially during the dark hours of the night. Therefore, the focus of this article is determining the range of visibility of dipped (low-beam) headlights under specific experimental conditions. We also designed a methodical guideline aimed at identifying the distance at which dipped headlights illuminate the road while a vehicle is in motion. Research conducted on various classes of road confirmed that the Hyundai i40 is best used on higher-class roads, while the Dacia Sandero is better used on lower-class roads due to the shape and spreading out of its light cone. Furthermore, the pros and cons of the distribution of light cones on several classes of road are presented. Sensor-related equipment was also used to investigate light beam afterglow. In particular, an LX-1108 light meter was applied to determine the obstacle illumination intensity, the properties of which enable recording of low lighting values, and a DJI Mavic AIR 2 unmanned aerial vehicle (UAV; drone) was utilized to record the data related to the location of the examined vehicle, as well as light afterglow at night; relevant data evaluation was carried out using Inkscape software.
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Road traffic accidents (hereinafter referred to as accidents) are currently a significant problem in the field of road safety in the Russian Federation. In addition to significant material damage, ...they often entail human losses. In this regard, the issue of investigating an accident is acute, as well as conducting road transport examinations. Modern calculation models do not always allow to determine the mechanism of an accident comprehensively and fully, especially at the final stage of the mechanism, when the vehicle continued its movement after interacting with an obstacle. The task becomes more complicated in cases where the movement of the vehicle did not have adequate calculation models in the specialized literature for specific (particular) cases of movement. Therefore, the construction of a functional universal computational model of complex vehicle movement based on basic physical principles within the framework of road transport expertise is an urgent task.
Road traffic crashes cause social, economic, physical and emotional losses. They also reduce operating speed and road capacity and increase delays, unreliability, and productivity losses. Previous ...crash duration research has concentrated on individual crashes, with the contributing elements extracted directly from the incident description and records. As a result, the explanatory variables were more regional, and the effects of broader macro-level factors were not investigated. This is in contrast to crash frequency studies, which normally collect explanatory factors at a macro-level. This study explores the impact of various factors and the consistency of their effects on vehicle crash duration and frequency at a macro-level. Along with the demographic, vehicle utilisation, environmental, and responder variables, street network features such as connectedness, density, and hierarchy were added as covariates. The dataset contains over 95,000 vehicle crash records over 4.5 years in Greater Sydney, Australia. Following a dimension reduction of independent variables, a hazard-based model was estimated for crash duration, and a Negative Binomial model was estimated for frequency. Unobserved heterogeneity was accounted for by latent class models for both duration and frequency. Income, driver experience and exposure are considered to have both positive and negative impacts on duration. Crash duration is shorter in regions with a dense road network, but crash frequency is higher. Highly connected networks, on the other hand, are associated with longer length but lower frequency.
Impaired driving and legalization of recreational cannabis Windle, Sarah B; Sequeira, Crystal; Filion, Kristian B ...
CMAJ. Canadian Medical Association journal,
2021-Apr-06, 2021-04-06, 20210406, Volume:
193, Issue:
14
Journal Article
Peer reviewed
Open access
Windle et al analyze the association between recreational cannabis legalization and fatal motor vehicle collision rates and discuss the implications of existing evidence for Canadian policy and ...physicians' practice. Analyses of data suggest that legalization of recreational cannabis in US jurisdictions may be associated with a small but significant increase in fatal motor vehicle collisions and fatalities, which, if extrapolated to the Canadian context, could result in as many as 308 additional driving fatalities annually. Health care professionals have an opportunity to educate patients about the safer use of cannabis products, including advising against cannabis use and driving (especially in combination with alcohol), with a suggested wait time of at least 6 hours before driving.
There have been few longitudinal studies on Chinese bus drivers and the individual differences in the relationships between organizational justice and job satisfaction. This study examined the ...organizational justice and job satisfaction in bus drivers and the individual differences in this relationship.
A two-wave longitudinal study design was employed. A first survey was conducted on 513 Chinese bus drivers in October 2021 that collected socio-demographic information and asked about their perceptions of organizational fairness. A second survey was conducted six months later that asked about role overload and job satisfaction and assessed their proactive personality type. An effect model was then used to explore the moderating effects of role overload and proactive personality type on the relationships between organizational justice and job satisfaction.
Both procedural and interactive justice predicted the bus drivers' job satisfaction. Proactive personalities and role overload were found to enhance this relationship.
Organizations could benefit from screening at the recruitment stage for drivers with highly proactive personalities. Relevant training for drivers with low proactive personalities could partially improve employee job satisfaction. When viewed from a Chinese collectivist cultural frame, role overload could reflect trust and a sense of belonging, which could enhance job satisfaction. Finally, to improve employee job satisfaction, organizations need to ensure procedural and interactive justice.
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Traffic accidents have been one of the most important global public problems. It has caused a severe loss of human lives and property every year. Studying the influential factors of accidents can ...help find the reasons behind. This can facilitate the design of effective measures and policies to reduce the traffic fatality rate and improve road safety. However, most of the existing research either adopted methods based on linear assumption or neglected to further evaluate the spatial relationships. In this paper, we proposed a methodology framework based on XGBoost and grid analysis to spatially analyze the leading factors on traffic fatality in Los Angeles County. Characteristics of the collision, time and location, and environmental factors are considered. Results show that the proposed method has the best modeling performance compared with other commonly seen machine learning algorithms. Eight factors are found to have the leading impact on traffic fatality. Spatial relationships between the eight factors and the fatality rates within the Los Angeles County are further studied using the grid-based analysis in GIS. Specific suggestions on how to reduce the fatality rate and improve road safety are provided accordingly.
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