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

A hardware/software field programmable gate array （FPGA）-based driver system was proposed and demonstrated for the KAF-39000 large area high resolution charge coupled device （CCD）. The requirements of the KAF-39000 driver system were analyzed. The structure of ＂microprocessor with application specific integrated circuit （ASIC） chips＂ was implemented to design the driver system. The system test results showed that dual channels of imaging analog data were obtained with a frame rate of 0.87frame/s. The frequencies of horizontal timing and vertical timing were 22.9MHz and 28.7kHz, respectively, which almost reached the theoretical value of 24 MHz and 30kHz, respectively.

Simulation software for CRH2 and CRH3 traction driver systems based on SIMULINK and VC

Simulation models of traction driver systems were established using SIMULINK,according to the actual structure and parameters of China Railway High-Speed 2 (CRH2) and China Railway High-Speed 3 (CRH3) trains.In these models,the traction motor adopts transient current control and an indirect rotor magnetic field orientation vector control strategy,and the traction converter uses sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) methods.After these models are transformed in VC++ program,and a friendly interface and data processing system are constructed,simulation software is obtained for CRH2 and CRH3 traction driver systems.On this basis,the operational performance of a traction converter was simulated and analyzed at different train speeds and in different conditions.The simulation results can provide a reference for the actual design and production of a traction converter.

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.

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.

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.

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.

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.

Exploring Challenges in Developing a Smart and Effective Assistive System for Improving the Experience of the Elderly Drivers

As the overall population ages, driving-related accidents and injuries, associated with elderly drivers, have risen. Existing research about elderly drivers mainly focuses on factual data collection and analysis, indicating the elderly＇s growing fatal accident rates and their different behaviours compared to younger drivers. However, few research has focused on design-led practical solutions to mitigate the elderly＇s growing fatal accidents, by consid- ering their usability and body conditions, afflicting the elderly, such as decreased vision, hearing, and reaction times. In this paper, first, current worldwide situations on growing fatal accident rates for elderly drivers is reviewed and the key impact factors are identified and discussed with regarding to usability and design trend in the automotive technology for elderly. Second, existing smart vehicle technology-based solutions to promote safe driving are explored and their pros and cons are discussed and anal- ysed. Most of solutions are not created by people with driving difficulties, which are caused by health problems most commonly afflicting the elderly. Thirdly, diverse design-led research activities are taken, such as a survey, observation, and interviews to gain new understanding of what kinds of driving problems elderly drivers have and demonstrate how new system concepts could be developed for the elderly＇s benefits. Finally, it is found that the elderly＇s low vision and late reaction are main factors causing their driving difficulties. Based on this finding, usable vehicle system design ideas have been proposed, by utilising facial expression sensing technology as a solution. The proposed solutions would ensure reducing both the elderly＇s driving problems and high fatal accident rates and provide a more enjoyable driving environment for the elderly population.

FRS:Replication Driver Layer-Based Data Replication System on FreeBSD

Data replication is a key way to design a disaster tolerance system. This paper presents a replication driver layer-based data replication system on FreeBSD （FRS）. The system is embedded into the replication device driver layer, does not depend on specific storage devices and logical volume manager, and can achieve replication on data block level. The design considerations and decisions in defining FRS are described in detail.

Extreme events as ecosystems drivers： Ecological consequences of anomalous Southern Hemisphere weather patterns during the 2001/2002 austral spring-summer

The frequency and severity of extreme events associated with global change are both forecast to increase with a concomitant increase expected in perturbations and disruptions of fundamental processes at ecosystem, community and population scales, with potentially catastrophic consequences. Extreme events should thus be viewed as ecosystem drivers, rather than as short term deviations from a perceived ＇norm＇. To illustrate this, we examined the impacts associated with the extraordinary weather pattern of the austral spring/summer of 2001/2002, and find that patterns of ocean-atmosphere interactions appear linked to a suite of extreme events in Antarctica and more widely across the Southern Hemisphere. In the Antarctic, the extreme events appear related to particular ecological impacts, including the substantial reduction in breeding success of Ade1ie penguins at sites in the Antarctic Peninsula as well as for Adelie penguin and snow petrel colonies in East Antarctica, and the creation of new benthic habitats associated with the disintegration of the Larsen B Ice Shelf. Other major impacts occurred in marine and terrestrial ecosystems at temperate and tropical latitudes. The suite of impacts demonstrates that ecological consequences of extreme events are manifested at fundamental levels in ecosystem processes and produce long-term, persistent effects relative to the short-term durations of the events. Changes in the rates of primary productivity, species mortality, community structure and inter-specific interactions, and changes in trophodynamics were observed as a consequence of the conditions during the 2001/2002 summer. Lasting potential consequences include reaching or exceeding tipping points, trophic cascades and regime shifts.

Drivers of Change, Threats and Barriers to the Conservation of Biodiversity in Traditional Agricultural Systems

People and nature are working in tandem to reduce the integrity of traditional agricultural systems. The paper highlighted the drivers of change acting on traditional agricultural landscapes including the threats and barriers hindering biodiversity conservation. The drivers of change are： modem agriculture, tourism, education and outmigration and climate change. The threats are encroachment and land-use change, pollution, replacement of traditional varieties and erosion of indigenous knowledge systems and practices. The barriers to biodiversity conservation include the incapacity of farmers to respond to pressures affecting their traditional farming systems, absence of methodologies for integrating traditional and modem knowledge in biodiversity conservation and the multi-functionality of traditional agriculture not fully captured by the market. The conservation framework to guide stakeholders for in-situ management of biodiversity includes niche market development, revenue generation and cultural restoration. The study made use of data collected during the series of workshops conducted in 2005-2007 covering the Ifugao Rice Terraces of the Philippines. Secondary data from other traditional agricultural systems were integrated to enrich the discussion and write-up of this paper.

Key Technology Analysis for Driver Support Systems in Japan

This paper presents the technical survey and the trend analysis of the driver support technologies such as a pre-crush braking system in Japan. In the first part, Vehicle Intelligence to assist drivers is defined by two objective functions which are both TGA （Target Generation Agent） and TAA （Target Accomplishment Agent）. TAA is mainly based on the conventional technologies that are braking smoothly, or driving with lower fuel consumption. On the other hand, TGA has the intelligent function instead of human drivers. The actual TGA are explained using some concrete driver support systems. After that, Japanese market introduction date and evolution of driver support systems are discussed with clarifying cognitive aspects which are the perception support, the judgment support and the execution support. And Key technologies underlying evolution of driver support systems are explained. Finally the author concludes that the knowledge and insights needed for intelligent driver support systems will be much more complex than in the case of autonomous vehicles that drive themselves.