Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Driver identification in intelligent transport systems has immense demand,considering the safety and convenience of traveling in a vehicle.The rapid growth of driver assistance systems(DAS)and driver identification system propels the need for understanding the root causes of automobile accidents.Also,in the case of insurance,it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing.It is observed that drivers with frequent records of paying“fines”are compelled to pay higher insurance payments than drivers without any penalty records.Thus driver identification act as an important information source for the intelligent transport system.This study focuses on a similar objective to implement a machine learning-based approach for driver identification.Raw data is collected from in-vehicle sensors using the controller area network(CAN)and then converted to binary form using a one-hot encoding technique.Then,the transformed data is dimensionally reduced using the Principal Component Analysis(PCA)technique,and further optimal parameters from the dataset are selected using Whale Optimization Algorithm(WOA).The most relevant features are selected and then fed into a Convolutional Neural Network(CNN)model.The proposed model is evaluated against four different use cases of driver behavior.The results show that the best prediction accuracy is achieved in the case of drivers without glasses.The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors(KNN)and Support Vector Machines(SVM)models with and without using dimensionality reduction approaches.

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents.This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos.This model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames.The learned features are then used as the input of the LSTM component for modeling high-level temporal features.In addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN units.The BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation function.The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers.We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other.As a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively.

OSMR is a potential driver of inflammation in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a neurodegenerative disease,and the molecular mechanism underlying its pathology remains poorly understood.However,inflammation is known to play an important role in the development of this condition.To identify driver genes that affect the inflammatory response in amyotrophic lateral sclerosis,as well as potential treatment targets,it is crucial to analyze brain tissue samples from patients with both sporadic amyotrophic lateral sclerosis and C9orf72-related amyotrophic lateral sclerosis.Therefore,in this study we used a network-driven gene analysis tool,NetBID2.0,which is based on SJARACNe,a scalable algorithm for the reconstruction of accurate cellular networks,to experimentally analyze sequencing data from patients with sporadic amyotrophic lateral sclerosis.The results showed that the OSMR gene is pathogenic in amyotrophic lateral sclerosis and participates in the progression of amyotrophic lateral sclerosis by mediating the neuroinflammatory response.Furthermore,there were differences in OSMR activity and expression between patients with sporadic amyotrophic lateral sclerosis and those with C9orf72-related amyotrophic lateral sclerosis.These findings suggest that OSMR may be a diagnostic and prognostic marker for amyotrophic lateral sclerosis.

Leading Pattern of Spring Drought Variability over East Asia and Associated Drivers

Drought events have become more frequent and intense over East Asia in recent decades,leading to huge socioeconomic impacts.Although the droughts have been studied extensively by cases or for individual regions,their leading variability and associated causes remain unclear.Based on the Standardized Precipitation Evapotranspiration Index(SPEI)and ERA5 reanalysis product from 1979 to 2020,this study evealuates the severity of spring droughts in East Asia and investigates their variations and associated drivers.The results indicate that North China and Mongolia have experienced remarkable trends toward dryness during spring in recent decades,while southwestern China has witnessed an opposite trend toward wetness.The first Empirical Orthogonal Function mode of SPEI variability reveals a similar seesawing pattern,with more severe dryness in northwestern China,Mongolia,North China,South Korea,and Japan but increased wetness in Southwestern China and southeast Asia.Further investigation reveals that the anomalously dry(wet)surface in North(Southwestern)China is significantly associated with anomalously high(low)temperature,less(more)precipitation,and reduced(increased)soil moisture during the previous winter and early spring,regulated by an anomalous anticyclone(cyclone)and thus reduced(increased)water vapor convergence.The spring dry-wet pattern in East Asia is also linked to cold sea surface temperature anomalies in the central-eastern Pacific.The findings of this study have important implications for improving the prediction of spring drought events in East Asia.

Knowledge, Attitudes and Practices Relating to HIV among Motorbike Taxi Drivers in Parakou, Benin, in 2021

Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect@ applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.

A Framework for Driver DrowsinessMonitoring Using a Convolutional Neural Network and the Internet of Things

One of the major causes of road accidents is sleepy drivers.Such accidents typically result in fatalities and financial losses and disadvantage other road users.Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system.Most studies have examined how the mouth and eyelids move.However,this limits the system’s ability to identify drowsiness traits.Therefore,this study designed an Accident Detection Framework(RPK)that could be used to reduce road accidents due to sleepiness and detect the location of accidents.The drowsiness detectionmodel used three facial parameters:Yawning,closed eyes(blinking),and an upright head position.This model used a Convolutional Neural Network(CNN)consisting of two phases.The initial phase involves video processing and facial landmark coordinate detection.The second phase involves developing the extraction of frame-based features using normalization methods.All these phases used OpenCV and TensorFlow.The dataset contained 5017 images with 874 open eyes images,850 closed eyes images,723 open-mouth images,725 closed-mouth images,761 sleepy-head images,and 1084 non-sleepy head images.The dataset of 5017 images was divided into the training set with 4505 images and the testing set with 512 images,with a ratio of 90:10.The results showed that the RPK design could detect sleepiness by using deep learning techniques with high accuracy on all three parameters;namely 98%for eye blinking,96%for mouth yawning,and 97%for head movement.Overall,the test results have provided an overview of how the developed RPK prototype can accurately identify drowsy drivers.These findings will have a significant impact on the improvement of road users’safety and mobility.

Seroprevalence Survey of HIV and Hepatitis B Virus and Behavioral Characteristics among Heavy Truck Drivers along Port Sudan-Khartoum Highways

The prevalence of human immunodeficiency virus (AIDS) and hepatitis B virus among heavy truck drivers and their assistants has been well documented globally in correlation with their behavioral characteristics. The present study aimed to screen for human immunodeficiency virus (HIV), hepatitis B virus (HBV), and behavioral characteristics among heavy truck drivers in Port Sudan. A cross-sectional study was conducted on 274 heavy truck drivers and their assistants who used the highway Port Sudan-Khartoum in Port Sudan city during 2019-2021. Data on behavioral characteristics and substance use habits were collected using a structured questionnaire, and an ELISA test was used to screen for HIV and HBV infections in the study participants. The chi-square test, odds ratio, and confidence intervals were used to find the association between behavioral characteristics and seropositive HIV/HBV. Of the 274 enrolled participants, the seroprevalence rates of HIV were 2.7% and HBV was 23.7%. Ninety-four (34.3%) of them had a history of high-risk sexual behavior outside of marriage;only two (0.7%) used condoms;14.2% of participants reported alcohol use;and 1.1% reported drug use. Univariate analysis revealed that having a sex history outside of marriage with ≥1 sex partner and never using a condom with a spouse or casual partner were significant risk factors for HIV and HBV among drivers. Fortunately, we found that most of the drivers reported low alcohol and drug use. Concerning this study, the seroprevalence of HIV and HBV is highly associated with a history of having sex outside of marriage and sexual behavior among truck drivers and assistances. Additional studies are needed to further investigate other STIs and behavioral characteristics associated with factors in truck drivers/assistance in different truck stop regions in Sudan.

Statistical Analysis of Accident Proneness of Drivers

A mathematical model describing the risks of road accidents has been built on the basis of statistical data of drivers’ accident rate. It has been revealed that drivers can be divided by the degree of their accident proneness into four categories with sharply differing probabilities of road accidents. It has been shown that there is a possibility of classification of drivers in accordance with specified categories.

Design of a Hardware/Software FPGA-Based Driver System for a Large Area High Resolution CCD Image Sensor

可编程的门数组(FPGA ) 基于的硬件 / 软件域驱动程序系统被建议并且高为 KAF-39000 大区域证明分辨率充电联合了设备(电荷耦合器件) 。KAF-39000 司机系统的要求被分析。有特定的集成电路(ASIC ) 削的申请的微处理器的结构被实现设计驱动程序系统。系统测试结果证明成像类似物数据的双隧道与 0.87 frame/s 的框架率被获得。分别地，水平预定并且垂直预定的频率是 22.9 MHz 和 28.7 kHz 它几乎分别地到达了 24 MHz 和 30 kHz 的理论价值。

Development and Application of the NT Device Driver in CNC System

The key technology of open architecture CNC systems kernel device driver,including the interrupt mechanism, I/O subsystem, the structure of device driver and the communication between the driver and the application program, is discussed in the paper and a specific application is given at the end.

The Extent and Drivers of Deforestation and Forest Degradation in Masito-Ugalla Ecosystem, Kigoma Region, Tanzania

Deforestation and forest degradation has been observed to be rampant in Masito-Ugalla ecosystem, Kigoma Region, western part of Tanzania. This paper therefore, intended to assess the extent of deforestation and forest degradation in the area, and to determine their causes. A total of 101 respondents were considered as the sample size for this study. The methods used for data collection were household questionnaire interviews, in-depth interviews, focus group discussions, analysis of satellite images and direct observation. The findings indicated that deforestation was occurring in the study area. Satellite data revealed diminished closed woodland, bushed grassland, forest and thickets between 1990 and 2014. On the contrary, settlement area, cultivated land and open woodland had increased during the same time frame. Proximate factors causing deforestation and forest degradation included agricultural expansion, wood extraction and expansion of settlement area. Underlying factors included population growth, poverty, poor levels of education, lack of employment, corruption and embezzlement of public funds by politicians and senior government officials;and high demand for fuel-wood. Biophysical drivers like incidences of unplanned wildfires and socio trigger events notably civil strife were also important. In order to minimize the problem and based on the factors augmenting deforestation and forest degradation in the Masito-Ugalla ecosystem and their coupled negative consequences, effective environmental conservation education, increased patrols, effective law enforcement and provision of alternative energy sources are necessary.

FPGA-Based Traffic Sign Recognition for Advanced Driver Assistance Systems

This paper presents the implementation of an embedded automotive system that detects and recognizes traffic signs within a video stream. In addition, it discusses the recent advances in driver assistance technologies and highlights the safety motivations for smart in-car embedded systems. An algorithm is presented that processes RGB image data, extracts relevant pixels, filters the image, labels prospective traffic signs and evaluates them against template traffic sign images. A reconfigurable hardware system is described which uses the Virtex-5 Xilinx FPGA and hardware/software co-design tools in order to create an embedded processor and the necessary hardware IP peripherals. The implementation is shown to have robust performance results, both in terms of timing and accuracy.

Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion

Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock.The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard.The landmarks of the driver’s face were labeled and the eye-area was segmented.By calculating the aspect ratios of the eyes,the duration of eye closure,frequency of blinks and PERCLOS of both colored and infrared,fatigue can be detected.Based on the change of light intensity detected by a photosensitive device,the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection.Video samples of the driver’s face were recorded in the test vehicle.After training the classification model,the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime.

Vehicles, Advanced Features, Driver Behavior, and Safety: A Systematic Review of the Literature

Driver errors contribute to more than 94% of traffic crashes. Automotive companies are striving to enhance their vehicles to eliminate driver errors and reduce the number of crashes. Various advanced features like lane departure warning (LDW), blind spot warning (BSW), over speed warning (OSW), forward collision warning (FCW), lane keep assist (LKA), adaptive cruise control (ACC), cooperative ACC (CACC), and automated emergency braking (AEB) are designed to assist with, or in some cases take over, certain driving maneuvers. They can be broadly categorized into advanced driver assistance system (ADAS) and automated features. Each of these advanced features focuses on addressing a particular task of driving, thereby, aiding the driver, influencing their behavior, and enhancing safety. Many vehicles with these advanced features are penetrating into the market, yet the total reported number of crashes has increased in recent years. This paper presents a systematic review of these advanced features on driver behavior and safety. The review is categorized into 1) survey and mathematical methods to assess driver behavior, 2) field test methods to assess driver behavior, 3) microsimulation methods to assess driver behavior, 4) driving simulator methods to assess driver behavior, and 5) driver understanding and the effectiveness of advanced features. It is followed by conclusions, knowledge gaps, and need for further research.

Driver State Detection Based on Cardiovascular System and Driver Reaction Information Using a Graphical Model

Traffic accidents are mainly caused by human error. In an aging society, the number of accidents attributed to elderly drivers is increasing. One noteworthy reason for this is operation misapplication. Studies have been conducted on the use of human-machine interfaces (HMIs) to inform the driver when he or she makes an error and encourage appropriate actions. However, the driver state during the erroneous action has not been investigated. The purpose of this study is to clarify the difference in the driver’s state between normal and surprising situations in a misapplication scenario, utilizing multimodal information such as biometric information and driver operation. We found significant changes in the interaction of components between the normal and the surprised driving state. The results could provide basic knowledge for the future development of a driver assistance system and driver state estimation using data acquired from multiple sensors in the vehicle.

Visualizing Relationships between Drivers of Environmental Change and Pressures on Land-Based Ecosystems

The pursuit of human needs and demands is placing more pressure on land resources than ever before. The challenge of feeding 7 billion people is increasingly competing with rising demands for materials and biofuels. Deforestation and land degradation are among the pressing outcomes of these trends. Drivers of environmental change—including population growth, economic activity, consumption, urbanization, trade, conflict, and governance—clearly play a role in aggravating or mitigating these pressures on land. Despite advances in understanding causality in complex systems, navigating the interactions between these drivers remains a major challenge. This paper analyzes and visualizes the relationships between multiple, interacting drivers of environmental change and specific pressures on land-based ecosystems. Drawing on experience from the development of the Drivers and Land chapters of the UN Environment Programme’s Fifth Global Environment Outlook report (GEO-5), we use a series of Kiviat diagrams to illustrate the relative influence of key drivers on selected pressures on land. When individual diagrams are overlaid, patterns of influence emerge that can provide insight into where policy responses might best be targeted. We propose that, subject to some limitations, the Kiviat exercise can provide an accessible and potentially valuable “knowledge-intermediary” tool to help link science-based information to policy action.

Support Systems Designed for Older Drivers to Achieve Safe and Comfortable Driving

Background: The number of older people is increasing. Many of them expect to maintain a rich social life and to continue driving at an older age. Objective: The present study investigates the mechanisms behind self-regulation and driving cessation in order to suggest development of support systems to prolong older drivers’ safe mobility. Method: Three focus groups were conducted with 19 older active drivers aged 65+ who were divided according to annual mileage driven. Results: A content analysis revealed broad self-regulatory behaviour as already reported in the literature, e.g., avoiding driving at rush hour and at night. The participants also reported difficulty in finding the way to their final destination and an increasing need to plan their travelling. Co-piloting was a behaviour applied by couples to cope with difficulties encountered in traffic. A large part of the discussion was focused on emerging feelings of stress, anxiety and fear when driving in recent years, a feeling induced by external factors e.g., other road users’ behaviour, traffic density or high speed. Apart from health problems, high levels of stress could explain driving cessation, especially for women. An increased feeling of safety and comfort could be achieved by an increased use of support systems specifically designed to respond to older drivers’ needs. Conclusion: Support systems for older drivers should increase comfort and decrease their stress levels. New systems, such as co-pilot function and more developed Global Positioning System (GPS) supporting of the entire travel from door to door, should be developed to respond to the market needs.

Development of Data Acquisition Card Driver for ICRH System on EAST

Presented in this paper is the development of the driver for the data acquisition card with a peripheral component interconnection （PCI） local bus on the ion cyclotron range of frequency heating （1CRH） system. The driver is mainly aimed at the embedded VxWorks system （real-time operating system） which is widely used in various fields of real-time systems. An efficient way is employed to develop this driver, which will advance the real-time control of the ICRH system on the experimental advanced superconductor tokamak （EAST）. The driver is designed using the TORNADO integrated development environment （IDE）, and implemented in C plus language. The details include the hardware configuration, analogue/digital （A/D） and digital/analogue （D/A） conversion, input and output （I/O） operation of the driver to support over five cards. The data acquisition card can be manipulated in a low-level program and meet the requirements of A/D conversion and D/A outputs.

Standardized Evaluation of Camera-based Driver State Monitoring Systems

Driver state sensing technologies, such as vehicular systems, start to be widely considered by automotive manufacturers. To reduce the cost and minimize the intrusiveness towards driving, the majority of these systems rely on the in-cabin camera(s) and other optical sensors. With their great capabilities in detecting and intervening of driver distraction and inattention,these technologies may become key components in future vehicle safety and control systems. However, to the best of our knowledge,currently, there is no common standard available to objectively compare the performance of these technologies. Thus, it is imperative to develop one standardized process for evaluation purposes.In this paper, we propose one systematic and standardized evaluation process after successfully addressing three difficulties:1) defining and selecting the important influential individual and environmental factors, 2) countering the effects of individual differences and randomness in driver behaviors, and 3) building a reliable in-vehicle driver head motion tracking tool to collect ground-truth motion data. We have collected data on a large scale on a commercial driver state-sensing platform. For each subject, 30 to 40 minutes of head motion data was collected and included variables, such as lighting conditions, head/face features,and camera locations. The collected data was analyzed based on a proposed performance measure. The results show that the developed process can efficiently evaluate an individual camerabased driver state sensing product, which builds a common base for comparing the performance of different systems.

Exploring the Effectiveness of Gesture Interaction in Driver Assistance Systems via Virtual Reality

Dear editor,This letter presents a user study to explore the effectiveness of gesture interaction in driver assistance system(DAS).Distracted driving is a specific form of driver inattention and distraction occurs when the drivers'attention is diverted from the driving to other activities.