Journal articles: 'Driving database'

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Kasprzyk, A. "BioMart: driving a paradigm change in biological data management." Database 2011 (November 13, 2011): bar049. http://dx.doi.org/10.1093/database/bar049.

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Kossmann, Jan, and Rainer Schlosser. "Self-driving database systems: a conceptual approach." Distributed and Parallel Databases 38, no. 4 (March 16, 2020): 795–817. http://dx.doi.org/10.1007/s10619-020-07288-w.

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Abstract Challenges for self-driving database systems, which tune their physical design and configuration autonomously, are manifold: Such systems have to anticipate future workloads, find robust configurations efficiently, and incorporate knowledge gained by previous actions into later decisions. We present a component-based framework for self-driving database systems that enables database integration and development of self-managing functionality with low overhead by relying on separation of concerns. By keeping the components of the framework reusable and exchangeable, experiments are simplified, which promotes further research in that area. Moreover, to optimize multiple mutually dependent features, e.g., index selection and compression configurations, we propose a linear programming (LP) based algorithm to derive an efficient tuning order automatically. Afterwards, we demonstrate the applicability and scalability of our approach with reproducible examples.

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Lockemann, Peter C., Alfons Kemper, and Guido Moerkotte. "Future database technology: driving forces and directions." Future Generation Computer Systems 7, no. 1 (October 1991): 41–54. http://dx.doi.org/10.1016/0167-739x(91)90015-p.

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NAGIRI, Sueharu, Yasushi AMANO, and Syun'ichi DOI. "Performance Analysis and Database of Driving Maneuvers by using a Driving Simulator." Transactions of the Japan Society of Mechanical Engineers Series C 64, no. 627 (1998): 4338–44. http://dx.doi.org/10.1299/kikaic.64.4338.

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YAMAGISHI, Misako, Makoto INAGAMI, Takahiro TANAKA, Takashi YONEKAWA, Naoko KAWANO, Toshihisa SATO, Motoyuki AKAMATSU, and Hirofumi AOKI. "Driving Characteristics Database for Driving Assessment of Older Drivers: Data Repository for Human Life Driving Anatomy (Dahlia)." Japanese Journal of Ergonomics 56, no. 1 (February 15, 2020): 1–10. http://dx.doi.org/10.5100/jje.56.1.

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FUJINO, Naoki, Motoki SHINO, Machiko HIRAMATSU, Hideaki INOUE, Ryota SHIRATO, and Minoru KAMATA. "3207 Analysis on the deviant state while driving based on the driving behavior database." Proceedings of the Transportation and Logistics Conference 2009.18 (2009): 295–98. http://dx.doi.org/10.1299/jsmetld.2009.18.295.

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Mustafa, Rashed, Md Javed Hossain, and Thomas Chowdhury. "A better way for finding the optimal number of nodes in a distributed database management system." Daffodil International University Journal of Science and Technology 4, no. 2 (February 10, 2010): 19–22. http://dx.doi.org/10.3329/diujst.v4i2.4362.

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Distributed Database Management System (DDBMS) is one of the prime concerns in distributed computing. The driving force of development of DDBMS is the demand of the applications that need to query very large databases (order of terabytes). Traditional Client- Server database systems are too slower to handle such applications. This paper presents a better way to find the optimal number of nodes in a distributed database management systems. Keywords: DDBMS, Data Fragmentation, Linear Search, RMI. DOI: 10.3329/diujst.v4i2.4362 Daffodil International University Journal of Science and Technology Vol.4(2) 2009 pp.19-22

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Smith, David L., Wassim G. Najm, and Richard A. Glassco. "Feasibility of Driver Judgment as Basis for a Crash Avoidance Database." Transportation Research Record: Journal of the Transportation Research Board 1784, no. 1 (January 2002): 9–16. http://dx.doi.org/10.3141/1784-02.

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A crash avoidance database structure that is based on driver judgments is proposed. The structure comprises four driving conflict states (low risk, conflict, near crash, and crash) that correspond with advisory warning, crash-imminent warning, and crash mitigation counter-measures. The feasibility of this database structure is investigated by answering two questions: ( a) Can the driving states be reliably quantified? and ( b) Can the quantified states be used to create a useful crash avoidance database? The feasibility discussion centers on a specific dynamic scenario that involved braking maneuvers by a following vehicle to avoid a rear-end crash with a stopped lead vehicle. The quantification of driver judgment data from a controlled test track study is discussed as a foundation to identify rough quantitative locations for the conflict and near-crash state transitions, and crash data from a driving simulator experiment are used to estimate the crash state boundary. A database of on-road, naturalistic driving data is compared with the controlled experiments to evaluate the results. The method is found to be feasible, and recommendations for further development are presented.

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Alawneh, Ahmed Shlash, Osama Nusier, Abdullah I. Husein Malkawi, and Mustafa Al-Kateeb. "Axial compressive capacity of driven piles in sand: a method including post-driving residual stresses." Canadian Geotechnical Journal 38, no. 2 (April 1, 2001): 364–77. http://dx.doi.org/10.1139/t00-104.

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In this paper, empirical formulae were developed between the well-known pile bearing capacity factors (Nq and β) and parameters which include friction angle of sand, relative density, average effective vertical stress, and deformability of the soil below the pile toe. The developed empirical formulae were totally based on a database comprised of 28 well-documented compressive pile load tests collected exclusively from geotechnical literature. The actual measurements of shaft and end-bearing resistances of each pile in the database were adjusted to account for post-driving residual loads. Calculation of pile bearing capacity factors (Nq and β) was based on the adjusted shaft and end-bearing resistances rather than the actual unadjusted measured resistances for residual loads. Comparison of predicted and measured compressive capacity of an independent database comprised of 18 pile load tests showed that the developed formulae yield a reasonably accurate estimate of compressive pile capacity in cohesionless soils.Key words: driven piles, residual load, toe resistance, shaft resistance.

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Mori, Nobuhiro, Yasuhiro Kawahara, Tomokazu Tsuchiya, Kosuke Sekine, Takao Hara, and Teruhiko Takakura. "Analysis of driving pressure using ventilator management database in ARDS." Iryou kikigaku (The Japanese journal of medical instrumentation) 89, no. 6 (2019): 487–93. http://dx.doi.org/10.4286/jjmi.89.487.

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Atchley, Paul, Ashleigh V. Tran, and Mohammad Ali Salehinejad. "Constructing a publically available distracted driving database and research tool." Accident Analysis & Prevention 99 (February 2017): 306–11. http://dx.doi.org/10.1016/j.aap.2016.12.005.

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Chen, Kuan-Ting, and Huei-Yen Winnie Chen. "Driving Style Clustering using Naturalistic Driving Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 12, 2019): 176–88. http://dx.doi.org/10.1177/0361198119845360.

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Knowledge of driving styles may contribute to traffic safety, riding experience, and support the design of advanced driver-assistance systems or highly automated vehicles. This study explored the possibility of identifying driving styles directly from driving parameters using data from the Strategic Highway Research Program 2 database. Partitioning Around Medoids method was implemented to cluster driving styles based on 14 variables derived from time series records. Principal component analysis was then conducted to understand the underlying structure of the clusters and provide visualization to aid interpretation. Three clusters of driving styles were identified, for which the influential differentiating factors are speed maintained, lateral acceleration maneuver, braking, and longitudinal acceleration. Chi-square test of homogeneity was performed to compare the proportions of trips assigned to the three driving style clusters across levels of each driver attribute (age, gender, driving experience, and annual mileage). The results showed that all four attributes examined had an impact on how the trips were clustered, thus suggesting that the clusters capture individual differences in driving styles to some extent. While our results demonstrate the potential of naturalistic vehicle kinematics in capturing individuals’ driving styles, it was also possible that the identified clusters were classifying mostly drivers’ transient behaviors rather than habitual driving styles. More vehicle parameters and information about road conditions are necessary to obtain deeper insights into driving styles.

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Pál, Márton, Fanni Vörös, István Elek, and Béla Kovács. "Possibilities of high precision GPS data in autonomous driving." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-286-2019.

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Abstract. A self-driving car is a vehicle that is able to perceive its surroundings and navigate in it without human action. Radar sensors, lasers, computer vision and GPS technologies help it to drive individually (Figure 1). They interpret the sensed information to calculate routes and navigate between obstacles and traffic elements.Sufficiently accurate navigation and information about the current position of the vehicle are indispensable for transport. These expectations are fulfilled in the case of a human driver: the knowledge on traffic rules and signs make possible to navigate through even difficult situations. Self-driving systems substitute humans by monitoring and evaluating the surrounding environment and its objects without the background information of the driver. This analysing process is vulnerable. Sudden or unexpected situations may occur but high precision navigation and background GPS databases can complement sensor-detected data.The assistance of global navigation has been used in cars for decades. Drivers can easily plan their routes and reach their destination by using car GPS units. However, these devices do not provide accurate positioning: there may be a difference of several metres from the real location. Self-driving cars also use navigation to complement sensor data. Although there are already autonomous system tests on motorways and countryside roads, in densely built-in areas this technology faces complications due to accuracy problems. The dilution of precision (DOP) values can be extremely high in larger settlements because high buildings may hide southern sky (where satellite signs are sensed from on our latitude).We can achieve centimetre-level accuracy (if the conditions are ideal) with geodesic RTK (real-time kinematic) GPS systems. This high-precision position data is derived from satellite-based positioning systems. Measurements of the phase of the signal’s carrier wave are real-time corrected by a single reference or an interpolated virtual station.In this research we use RTK GPS technology in order to work out a spatial database. These measurements can also be less precise in dense cities, but there is time during fieldwork to try to eliminate inaccuracy. We have chosen a sample area in the inner city of Budapest, Hungary where we located all traffic signs, pedestrian crossings and other important elements. As self-driving cars need precise position data of these terrain objects, we have tried to work with a maximum error of a few decimetres.We have examined online map providers if they have feasible data structure and some base data. The implemented structure is similar to OpenStreetMap DB, in which there are already some traffic lights in important crossings. With this preliminary test database, we would like to filter out dangerous situations. If the camera of the car does not see a traffic sign because of a tree or a truck, information about it will be available from the database. If a pedestrian crossing is hardly visible and the sensor does not recognize it, the background GIS data will warn the car that there may be inattentive people on the road.A test application has also been developed (Figure 2.), in which our Postgres/Postgis database records have been inserted. In the next phase of the project we try to test our database in the traffic. We plan to drive through the sample area and observe the GPS accuracy in the recognition of the located signs.This research aims to achieve higher safety in the field of autonomous driving. By having a refreshable cartographic GIS database in the memory of a self-driving car, there is a smaller chance of risking human life. However, the maintenance demands a high amount of work. Because of this we should concentrate only on the most important signs. Even the cars can be able to supervise the content of the database if there is a large number of them on the road. The frequent production and analysis of point clouds is also an option to get nearer to safe automatized traffic.

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Kataoka, Hirokatsu, Yutaka Satoh, Yoshimitsu Aoki, Shoko Oikawa, and Yasuhiro Matsui. "Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database." Sensors 18, no. 2 (February 20, 2018): 627. http://dx.doi.org/10.3390/s18020627.

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Li, Yingfeng, Ronald Gibbons, and Alejandra Medina. "Integrating Adaptive Lighting Database with SHRP 2 Naturalistic Driving Study Data." Transportation Research Record: Journal of the Transportation Research Board 2526, no. 1 (January 2015): 1–9. http://dx.doi.org/10.3141/2526-01.

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The limited information in traditional safety data makes exploration of the complex interaction between characteristics of roadway lighting and crash risks and driver behavior difficult, yet this knowledge is needed by transportation agencies that want to reduce energy consumption while maintaining safety. Data from the SHRP 2 Naturalistic Driving Study (NDS) have provided researchers a powerful tool for safety analysis. In addition, the availability of a massive adaptive lighting database (ALD) has made it possible to evaluate and improve the use of roadway lighting as a safety countermeasure. This paper integrates ALD and NDS data and provides insight into how other spatially related data can be integrated with the NDS data to cater to the needs of safety studies. The ALD includes detailed in situ roadway lighting measurements for more than 2,000 mi of major roads in seven states. This paper fills a gap in NDS-related documentation by detailing the NDS database structure and elements. An efficient spatial approach for integrating NDS data with ALD or other large roadway-related data sets in a similar format is presented. The approach uses a combination of existing and custom geographic information system tools, including a hybrid tool that uses attributes to perform the ArcGIS Near function.

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Zhussupbekov, A. Zh, N. T. Alibekova, S. B. Akhazhanov, N. U. Shakirova, and A. B. Alpyssova. "Development geoinformation database for optimization length of driving and boring piles." Japanese Geotechnical Society Special Publication 6, no. 2 (April 30, 2019): 92–94. http://dx.doi.org/10.3208/jgssp.v06.giz14.

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Gerber, Jonathan P., and Louis I. Landau. "Driving change in rural workforce planning: the Medical Schools Outcomes Database." Australian Journal of Primary Health 16, no. 1 (2010): 36. http://dx.doi.org/10.1071/py09049.

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The Medical Schools Outcomes Database (MSOD) is an ongoing longitudinal tracking project of medical students from all medical schools in Australia and New Zealand. It was established in 2005 to track the career trajectories of medical students and will directly help develop models of workforce flow, particularly with respect to rural and remote shortages. This paper briefly outlines the MSOD project and reports on key methodological factors in tracking medical students. Finally, the potential impact of the MSOD on understanding changes in rural practice intentions is illustrated using data from the 2005 pilot cohort (n = 112). Rural placements were associated with a shift towards rural practice intentions, while those who intended to practice rurally at both the start and end of medical school tended to be older and interested in a generalist career. Continuing work will track these and future students as they progress through the workforce, as well as exploring issues such as the career trajectories of international fee-paying students, workforce succession planning, and the evaluation of medical education initiatives.

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Sun, Yuan, Hao Xu, Jianqing Wu, Elie Y. Hajj, and Xinli Geng. "Data Processing Framework for Development of Driving Cycles with Data from SHRP 2 Naturalistic Driving Study." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (January 2017): 50–56. http://dx.doi.org/10.3141/2645-06.

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In the modeling of vehicle operation costs, a driving cycle is a representative speed–time profile to describe the speed–acceleration pattern of a specific road scenario. Driving cycles are important input for estimation of fuel consumption and polluting emissions. Existing driving cycles are either from out-of-date driving data or without detailed consideration of influencing road properties because of the limitations of available data sets. As part of a project sponsored by FHWA, this research developed a data processing framework for development of driving cycles with data from both the SHRP 2 Naturalistic Driving Study (NDS) and the SHRP 2 Roadway Information Database (RID). The framework included data processing of NDS and RID data and a new synthetic optimization method to generate optimized representative driving cycles. The documented data processing framework was applied to develop the driving cycles of light-duty vehicles for 395 road scenarios with consideration of 10 road properties that could have influenced traffic speed patterns. The 4,400 NDS trips, each of which was at least 20 min long, were used for the development of driving cycles. This data processing frame can be applied for development of driving cycles for more road scenarios with data similar to those in the SHRP 2 database.

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Jeong, Mira, and Byoung Chul Ko. "Driver’s Facial Expression Recognition in Real-Time for Safe Driving." Sensors 18, no. 12 (December 4, 2018): 4270. http://dx.doi.org/10.3390/s18124270.

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In recent years, researchers of deep neural networks (DNNs)-based facial expression recognition (FER) have reported results showing that these approaches overcome the limitations of conventional machine learning-based FER approaches. However, as DNN-based FER approaches require an excessive amount of memory and incur high processing costs, their application in various fields is very limited and depends on the hardware specifications. In this paper, we propose a fast FER algorithm for monitoring a driver’s emotions that is capable of operating in low specification devices installed in vehicles. For this purpose, a hierarchical weighted random forest (WRF) classifier that is trained based on the similarity of sample data, in order to improve its accuracy, is employed. In the first step, facial landmarks are detected from input images and geometric features are extracted, considering the spatial position between landmarks. These feature vectors are then implemented in the proposed hierarchical WRF classifier to classify facial expressions. Our method was evaluated experimentally using three databases, extended Cohn-Kanade database (CK+), MMI and the Keimyung University Facial Expression of Drivers (KMU-FED) database, and its performance was compared with that of state-of-the-art methods. The results show that our proposed method yields a performance similar to that of deep learning FER methods as 92.6% for CK+ and 76.7% for MMI, with a significantly reduced processing cost approximately 3731 times less than that of the DNN method. These results confirm that the proposed method is optimized for real-time embedded applications having limited computing resources.

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Shino, M., H. Yoshitake, M. Hiramatsu, T. Sunda, and M. Kamata. "Deviated state detection method in driving around curves based on naturalistic driving behavior database for driver assistance systems." International Journal of Automotive Technology 15, no. 5 (August 2014): 749–55. http://dx.doi.org/10.1007/s12239-014-0078-7.

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Lipar, Peter, Irena Strnad, Martin Česnik, and Tomaž Maher. "Development of Urban Driving Cycle with GPS Data Post Processing." PROMET - Traffic&Transportation 28, no. 4 (August 30, 2016): 353–64. http://dx.doi.org/10.7307/ptt.v28i4.1916.

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This paper presents GIS-based methodology for urban area driving cycle construction. The approach reaches beyond the frames of usual driving cycle development methods and takes into account another perspective of data collection. Rather than planning data collection, the approach is based on available in-vehicle measurement data post processing using Geographic Information Systems to manipulate the excessive database and extract only the representative and geographically limited individual trip data. With such data post processing the data was carefully adjusted to include only the data that describe representative driving in Ljubljana urban area. The selected method for the driving cycle development is based on searching for the best microtrips combination while minimizing the difference between two vectors; one based on generated cycle and the other on the database. Accounting for a large random sample of actual trip data, our approach enables more representative area-specific driving cycle development than the previously used techniques.

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Yahia, Sabiha Ould, and Sophie Loriette-Rougegrez. "Fuzzy Querying of Evolutive Situations: Application to Driving Situations." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 1 (January 20, 2005): 13–17. http://dx.doi.org/10.20965/jaciii.2005.p0013.

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The study of the human behavior in driving situations is of primary importance for the improvement of drivers security. This study is complex because of the numerous situations in which the driver may be involved. The objective of the CASSICE project (Symbolic Characterization of Driving Situations) is to elaborate a tool in order to simplify the analysis task of the driver's behavior. In this paper, we will mainly take an interest in the indexing and querying of a multimedia database including the numerical data and the video sequences relating to a type of driving situations. We will put the emphasis on the queries to this database. They are often complex because they are formulated according to criteria depending on time, space and they use terms of the natural language.

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Utsugi, Akio, and Motoyuki Akamatsu. "LANE-CHANGE BEHAVIOR MODELING OF LONG-HAUL TRUCK DRIVER USING DRIVING DATABASE." IFAC Proceedings Volumes 40, no. 16 (2007): 425–30. http://dx.doi.org/10.3182/20070904-3-kr-2922.00075.

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Dhahir, Bashar, and Yasser Hassan. "Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 17 (July 1, 2018): 83–95. http://dx.doi.org/10.1177/0361198118784384.

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Many studies have been conducted to develop models to predict speed and driver comfort thresholds on horizontal curves, and to evaluate design consistency. The approaches used to develop these models differ from one another in data collection, data processing, assumptions, and analysis. However, some issues might be associated with the data collection that can affect the reliability of collected data and developed models. In addition, analysis of speed behavior on the assumption that vehicles traverse horizontal curves at a constant speed is far from actual driving behavior. Using the Naturalistic Driving Study (NDS) database can help overcome problems associated with data collection. This paper aimed at using NDS data to investigate driving behavior on horizontal curves in terms of speed, longitudinal acceleration, and comfort threshold. The NDS data were valuable in providing clear insight on drivers’ behavior during daytime and favorable weather conditions. A methodology was developed to evaluate driver behavior and was coded in Matlab. Sensitivity analysis was performed to recommend values for the parameters that can affect the output. Analysis of the drivers’ speed behavior and comfort threshold highlighted several issues that describe how drivers traverse horizontal curves that need to be considered in horizontal curve design and consistency evaluation.

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Ding, Hui, and Yao Zong Liu. "Research on the Data Mining System Based on B/S Framework and Algrotithm." Applied Mechanics and Materials 380-384 (August 2013): 1558–62. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1558.

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with the development of computer information science and technology, the acquirement and access of data from huge database become more and more convenient. Not all the algorithm of different database is the same. Data mining finds the method of characteristic demand data from a large amount of information. It searches data according to relevance and clustering of data. This paper presents a new data mining system---B / S framework and establishes rules of data mining algorithms and mathematical models using fuzzy membership function and Apriority algorithm. This paper establishes a set of data mining system related with subjects learn of driving school taking the driving school for example and using multi-layer B / S framework mathematical model and results of driving candidates. It also finds the importance of each study subject. It proposes data reference for the order of the learn subjects of driving school which provides a theoretical reference for the study of data mining algorithms and systems.

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Trappey, Amy, Charles V. Trappey, Chia-Ming Chang, Routine R. T. Kuo, Aislyn P. C. Lin, and C. H. Nieh. "Virtual Reality Exposure Therapy for Driving Phobia Disorder: System Design and Development." Applied Sciences 10, no. 14 (July 15, 2020): 4860. http://dx.doi.org/10.3390/app10144860.

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Driving phobia is an anxiety disorder. People are greatly impaired in their daily lives when suffering from driving phobia disorders. The anxieties can be triggered under various conditions, such as driving over bridges, driving at high speeds, or driving in close proximity to large trucks. Traditional cognitive behavioral therapy (CBT) and exposure therapy are the most common approaches used in the treatment of psychological disorders, such as anxiety disorder (AD) and panic disorder (PD). This research focuses on virtual reality (VR)-based exposure therapy, called VRET, and describes the design and development of a system which uses alternating levels of fear-based driving scenarios that can be recorded and automatically adjusted to maximize exposure effectiveness without causing the subjects to panic. The proposed VRET integrates an advanced feedback database module for tracing and analyzing the system, along with the user’s bio-data to show the valid data collection of the system and its effectiveness for future use in clinical trials. The research conducts a system’s pre-test analysis using 31 subjects to demonstrate the effectiveness of the system. This research demonstrates the systematic development of the VRET for driving phobia disorder by depicting the system framework, key system modules, system integration, bio-database management, and pre-test data analysis to support our next research efforts in hospital-based clinical trials and for additional VRET development applications for clinical psychology.

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Purssell, Roy A., Mark Yarema, Jean Wilson, Ming Fang, Richard Simons, Sharon Kasic, Riyad B. Abu-Laban, Jeffrey Brubacher, and Ioana Lupu. "Proportion of injured alcohol-impaired drivers subsequently convicted of an impaired driving criminal code offence in British Columbia." CJEM 6, no. 02 (March 2004): 80–88. http://dx.doi.org/10.1017/s1481803500009039.

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ABSTRACT Background: Alcohol is a frequent contributing factor to motor vehicle collision injuries. Our objective was to determine the proportion of intoxicated drivers hospitalized following motor vehicle crashes who were subsequently convicted of an impaired driving criminal code offence. Methods: We reviewed British Columbia Trauma Registry records from Jan. 1, 1992, to Mar. 31, 2000, and identified drivers of motor vehicles who were hospitalized for treatment of crash-related injuries. Patient identifiers were then used to link with the Insurance Corporation of British Columbia’s (ICBC) contraventions database and the ICBC Traffic Accident System collisions database. Results: Of 6067 patients identified in the Trauma Registry, 4042 had not been administered a blood ethanol test, 209 had no driver’s licence match in the relevant databases and 119 died, leaving 1697 eligible patients. Mean age was 34 years, and 79.6% were male. The average Injury Severity Score was 20, the average hospital stay was 14 days and, among ethanol-positive patients, the mean ethanol level was 34.0 mmol/L (156.4 mg/dL). In patients with levels >17.3 mmol/L, the police had listed ethanol as a contributing factor in 70.6% of cases. Despite this, only 11.0% were convicted of impaired driving and 8.4% of another criminal offence; 10.7% received a 24-hour roadside prohibition, 3.9% received a 90-day administrative driving prohibition and 25.0% were convicted of a contravention of the Motor Vehicle Act. Forty-one percent were not convicted of any offence at all. Conclusions: Intoxicated drivers in British Columbia requiring hospitalization as a result of alcohol-related motor vehicle crashes are seldom convicted of impaired driving or other criminal code offences.

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Jahanfar, Shayesteh. "Driving habits – A Canadian study." Trauma 20, no. 3 (August 18, 2017): 225–29. http://dx.doi.org/10.1177/1460408617724816.

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Introduction Motor vehicle accidents are a significant cause of morbidity and mortality. Safe driving behavior constitutes proper use of seat belts as a driver and as a passenger. The correct use of seat belts has been shown to reduce death and injury following motor vehicle accidents by more than 50%. This study aims at investigating seat belt use and driving habits among Canadians. Method A population-based database from the Canadian Community Health Survey was analyzed. Result About 12% reported that they use seat belts most of the times, rarely or never and 27% of respondents were using cellphone while driving often or sometimes; 8% of respondents admitted to driving after 2 or more alcoholic drinks. Seat belt use in taxi passengers is much lower than in own cars, with 40% not using a seatbelt all of the time when in a taxi. Discussion and conclusions The major risk factors for not wearing seat belt as a passenger include age, education, ethnicity and income.

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Dávid, Réka Ágnes, Zoltán Barcza, Anikó Kern, Erzsébet Kristóf, Roland Hollós, Anna Kis, Martin Lukac, and Nándor Fodor. "Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data." Atmosphere 12, no. 8 (July 27, 2021): 963. http://dx.doi.org/10.3390/atmos12080963.

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Accurate estimation of the timing of intensive spring leaf growth initiation at mid and high latitudes is crucial for improving the predictive capacity of biogeochemical and Earth system models. In this study, we focus on the modeling of climatological onset of spring leaf growth in Central Europe and use three spring phenology models driven by three meteorological datasets. The MODIS-adjusted NDVI3g dataset was used as a reference for the period between 1982 and 2010, enabling us to study the long-term mean leaf onset timing and its interannual variability (IAV). The performance of all phenology model–meteorology database combinations was evaluated with one another, and against the reference dataset. We found that none of the constructed model–database combinations could reproduce the observed start of season (SOS) climatology within the study region. The models typically overestimated IAV of the leaf onset, where spatial median SOS dates were best simulated by the models based on heat accumulation. When aggregated for the whole study area, the complex, bioclimatic index-based model driven by the CarpatClim database could capture the observed overall SOS trend. Our results indicate that the simulated timing of leaf onset primarily depends on the choice of model structure, with a secondary contribution from the choice of the driving meteorological dataset.

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Shi, Qin, Bingjiao Liu, Qingsheng Guan, Lin He, and Duoyang Qiu. "A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle." Advances in Mechanical Engineering 12, no. 1 (January 2020): 168781401990105. http://dx.doi.org/10.1177/1687814019901054.

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In this article, an approach of driving cycle generation for battery electric vehicle is proposed based on genetic ant colony algorithm. The real-world traffic information is utilized to build up a local driving cycle database, in which definitions of the short trip and kinematic characteristic parameters are discussed to describe the driving cycle. A method of principal component analysis is taken as a preprocessor for reducing the dimension of driving cycle data. And then, genetic ant colony algorithm is used to classify the type of short trips and generate the driving cycle. The experimental results on board indicate that, compared with the Economic Commission for Europe driving cycle, the error of driving range and characteristic parameters tested by genetic ant colony driving cycle are reduced by 18.1% and 18.3%, respectively. Therefore, genetic ant colony driving cycle is a good candidate to test driving range of battery electric vehicle.

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Araluce, Javier, Luis M. Bergasa, Manuel Ocaña, Elena López-Guillén, Pedro A. Revenga, J. Felipe Arango, and Oscar Pérez. "Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental Scenarios." Sensors 21, no. 18 (September 18, 2021): 6262. http://dx.doi.org/10.3390/s21186262.

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Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver’s gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases.

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Lin, J., Guang Yu Tan, B. C. Liu, and Yi Ming Rong. "Development of Case Database of Fixtures Based on SolidWorks." Key Engineering Materials 426-427 (January 2010): 289–93. http://dx.doi.org/10.4028/www.scientific.net/kem.426-427.289.

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This work employs Visual Basic and Access to develop a case database management system for flexible fixtures using coding technology and parameter driving technology. The database management system of fixtures’ cases is built. The secondary development of SolidWorks is performed by VB 6.0, and the system is transplanted to SolidWorks. The system can realize the fixtures’ auto coding, storage, search, display and system management with simple operation and convenient maintenance. The system has a large flexibility as the case database can be expanded and the fixtures’ parameters can be modified according to actual situation of enterprise without limitations of the software.

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Yang, Nan Yue. "Research on the Method for Intelligent Autonomous Vehicle to Distinguish the Road in Virtual Traffic Environment." Applied Mechanics and Materials 249-250 (December 2012): 1228–33. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.1228.

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This paper studied how to build the intelligent autonomous vehicle in driving simulation system and let it distinguish the traffic road in virtual traffic environment based on Eon Studio. The method of using the database to store the information of virtual traffic roads was proposed. The result illustrated that by this means the intelligent autonomous vehicle could distinguish the complex traffic network accurately in the virtual traffic environment, which enhanced responsivity and stability of the driving simulation system and created a more realistic driving environment for users.

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Li, Jing, and Yu Jun Zhang. "Analysis of the Universal Embedded Database Model Based on TPMS." Applied Mechanics and Materials 416-417 (September 2013): 1507–11. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1507.

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In order to make the embedded system without large memory capacity and embedded operating system can use database to manage data for itself, this paper puts forward a universal model of embedded database combining the characteristic of embedded database. It studies the structure of the model and applies it to tire pressure monitoring system (TPMS). It discusses the hardware design, file frame, physical structure and logical structure of the embedded database of TPMS. Then it sets up a reliable, real-time, convenient database for TPMS. This database will manage the data which come from TPMS effectively when automobiles are running. It makes TPMS can analyze the status of driving car scientifically and provide reliable tire information.

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Shao, Faming, Xinqing Wang, Fanjie Meng, Jingwei Zhu, Dong Wang, and Juying Dai. "Improved Faster R-CNN Traffic Sign Detection Based on a Second Region of Interest and Highly Possible Regions Proposal Network." Sensors 19, no. 10 (May 17, 2019): 2288. http://dx.doi.org/10.3390/s19102288.

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Traffic sign detection systems provide important road control information for unmanned driving systems or auxiliary driving. In this paper, the Faster region with a convolutional neural network (R-CNN) for traffic sign detection in real traffic situations has been systematically improved. First, a first step region proposal algorithm based on simplified Gabor wavelets (SGWs) and maximally stable extremal regions (MSERs) is proposed. In this way, the region proposal a priori information is obtained and will be used for improving the Faster R-CNN. This part of our method is named as the highly possible regions proposal network (HP-RPN). Second, in order to solve the problem that the Faster R-CNN cannot effectively detect small targets, a method that combines the features of the third, fourth, and fifth layers of VGG16 to enrich the features of small targets is proposed. Third, the secondary region of interest method to enhance the feature of detection objects and improve the classification capability of the Faster R-CNN is proposed. Finally, a method of merging the German traffic sign detection benchmark (GTSDB) and Chinese traffic sign dataset (CTSD) databases into one larger database to increase the number of database samples is proposed. Experimental results show that our method improves the detection performance, especially for small targets.

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Guo, Feng. "Statistical Methods for Naturalistic Driving Studies." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 2019): 309–28. http://dx.doi.org/10.1146/annurev-statistics-030718-105153.

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The naturalistic driving study (NDS) is an innovative research method characterized by the continuous recording of driving information using advanced instrumentation under real-world driving conditions. NDSs provide opportunities to assess driving risks that are difficult to evaluate using traditional crash database or experimental methods. NDS findings have profound impacts on driving safety research, safety countermeasures development, and public policy. NDSs also come with attendant challenges to statistical analysis, however, due to the sheer volume of data collected, complex structure, and high cost associated with information extraction. This article reviews statistical and analytical methods for working with NDS data. Topics include the characteristics of NDSs; NDS data components; and epidemiological approaches for video-based risk modeling, including case-cohort and case-crossover study designs, logistic models, Poisson models, and recurrent event models. The article also discusses several key issues related to NDS analysis, such as crash surrogates and alternative reference exposure levels.

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Yao, Ying, Xiaohua Zhao, Chang Liu, Jian Rong, Yunlong Zhang, Zhenning Dong, and Yuelong Su. "Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones." Journal of Advanced Transportation 2020 (March 23, 2020): 1–11. http://dx.doi.org/10.1155/2020/9263605.

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Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.

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Hu, Lin, Xingqian Bao, Hequan Wu, and Wenguang Wu. "A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data." Journal of Advanced Transportation 2020 (June 2, 2020): 1–7. http://dx.doi.org/10.1155/2020/9084245.

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Traffic accidents are often related to the driver’s driving behavior, which is mainly decided by his or her characters. In order to explore the correlation of traffic accident risk with driver characters, the age, driving experience, and driving style were statistically analyzed based on the China In-Depth Accident Study (CIDAS) database. Taking the number of casualties in the accident as evaluation indicators, the grey cluster analysis was used to classify the drivers into four accident risk ranks: low, medium to low, medium to high, and high. The results show that drivers aged 18–30 years are more likely to induce accidents; drivers with 6–10 years of driving experience have the highest risk to accidents, followed by drivers with 4-5 years of driving experience; and the driving style is also highly correlated with accident risk tendency.

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Wang, Jianqiang, Yang Zheng, Xiaofei Li, Chenfei Yu, Kenji Kodaka, and Keqiang Li. "Driving risk assessment using near-crash database through data mining of tree-based model." Accident Analysis & Prevention 84 (November 2015): 54–64. http://dx.doi.org/10.1016/j.aap.2015.07.007.

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Qin, Ru Ming. "Design of Random Combination System Based on Five Common Kinds of Mechanical Transmission." Advanced Materials Research 383-390 (November 2011): 7475–79. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.7475.

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Introduce system’s overall function design concept and key technologies. The system is divided into driving module and driven module. According to the depth of calling, the system can also divide into four application modules, and is developed in coordination with Visual Basic 6.0, Visual FoxPro 6.0 and Visual LISP. Each module uses Windows and AutoCAD as executive support system. Under the control of interface program, the module can automatically execute and transmit data. Focus on the program interface design of the driving module. Users can be allowed to arrange and combine five common types of mechanical transmission. 3905 kinds of transmission scheme that is less than five levels can be got. The system can finish total design computation, work capability computation, tolerance database inquiry and drive part drawing drafting automatically and continuously. Mechanical design efficiency can be greatly improved.

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Naji, Hasan, Qingji Xue, Nengchao Lyu, Chaozhong Wu, and Ke Zheng. "Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model." Sustainability 10, no. 8 (August 13, 2018): 2868. http://dx.doi.org/10.3390/su10082868.

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With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.

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Ghasemzadeh, Ali, Britton E. Hammit, Mohamed M. Ahmed, and Hesham Eldeeb. "Complementary methodologies to identify weather conditions in naturalistic driving study trips: Lessons learned from the SHRP2 naturalistic driving study & roadway information database." Safety Science 119 (November 2019): 21–28. http://dx.doi.org/10.1016/j.ssci.2019.01.006.

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Hu, Lin, Xingqian Bao, Miao Lin, Chao Yu, and Fang Wang. "Research on risky driving behavior evaluation model based on CIDAS real data." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 8 (January 18, 2021): 2176–87. http://dx.doi.org/10.1177/0954407020985972.

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This paper constructs an evaluation system that reflects dangerous driving behavior. The evaluation system has a three-layer structure model of “Evaluation Index-Performance Mode-Driving behavior score.” Verification of the feasibility of the model based on the relationship between the driver and the cause of the accident based on behavioral characteristics. First, the driving return survey data and accident form information of the real traffic accident cases of China In-Depth Accident Study (CIDAS) database are counted, and the character variables are converted into digital variables. Then, a three-tier structure of the dangerous driving behavior evaluation system is built, and the correlation between the driver and the cause of the accident is conducted to verify the feasibility of the model. The research shows that the individual characteristics of drivers with dangerous driving behavior are closely related to the cause of accidents, and the evaluation system constructed in this paper can quantify and describe this relationship effectively.

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Hammit, Britton E., Ali Ghasemzadeh, Rachel M. James, Mohamed M. Ahmed, and Rhonda Kae Young. "Evaluation of weather-related freeway car-following behavior using the SHRP2 naturalistic driving study database." Transportation Research Part F: Traffic Psychology and Behaviour 59 (November 2018): 244–59. http://dx.doi.org/10.1016/j.trf.2018.08.023.

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Kim, Baeyoung, ChoongMin Jeong, and Myungwon Suh. "Study of a simulator database and an experiment for the evaluation of highway driving safety." Journal of Mechanical Science and Technology 25, no. 2 (February 2011): 537–42. http://dx.doi.org/10.1007/s12206-010-1228-0.

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McKissack, Haley M., Yvonne E. Chodaba, Tyler R. Bell, Eva J. Lehtonen, Ibukunoluwa B. Araoye, Ashish B. Shah, Despina Stavrinos, and Michael D. Johnson. "Prevalence and Safety of Left-Footed Driving in Right Foot Surgery Patients: A Retrospective Survey and Driving Simulation Study." Foot & Ankle Orthopaedics 4, no. 4 (October 1, 2019): 2473011419S0005. http://dx.doi.org/10.1177/2473011419s00056.

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Category: Post-operative Driving Introduction/Purpose: For many patients, returning to driving after right foot and ankle surgery is a concern, and it is not uncommon for patients to ask if driving may be performed with their left foot. A paucity of literature exists to guide physician recommendations for return to driving. The purpose of this study is to describe the driving habits of patients after right-sided foot surgery and assess the safety of left foot driving using a driving simulator. Methods: Patients who underwent right foot or ankle operations between January 2015 and December 2015 were retrospectively identified. A survey assessing driving habits prior to surgery and during the recovery period was administered via REDCap database through email or telephone. Additionally, simulated driving scenarios were conducted using a driving simulator in 20 patients to compare characteristics of left versus right foot driving. Results: Thirty six of 96 (37%) patients who responded to the survey reported driving with the left foot postoperatively. No trends were found associating left foot driving prevalence and socioeconomic status. In driving simulations, patients exceeded the speed limit significantly more (p<0.001) and hit other vehicles more (p<0.026) when driving with the right foot than the left. Time to fully brake and fully release the throttle in response to vehicular hazards were significantly prolonged in left foot driving compared to right (p = 0.019 and p = 0.034, respectively). Conclusion: A significant proportion of right foot and ankle surgery patients engaged in left foot driving during postoperative recovery. Driving with the left foot may prolong brake and throttle release times, and is therefore not recommended.

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Brunnauer, Alexander, and Gerd Laux. "Driving Under the Influence of Antidepressants: A Systematic Review and Update of the Evidence of Experimental and Controlled Clinical Studies." Pharmacopsychiatry 50, no. 05 (July 17, 2017): 173–81. http://dx.doi.org/10.1055/s-0043-113572.

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Abstract Introduction This review provides an update of experimental and clinical studies on the effects of antidepressants on driving performance. Methods A systematic literature search on the PubMed database (1980–2016) was performed. Results Twenty-eight studies could be included in this review, whereas only 5 studies investigated driving performance under antidepressants in patients. Most tri- and tetracyclics have acute deleterious effects on driving performance that, except for mianserin, attenuate after subchronic use. Selective serotonin reuptake inhibitors and the serotonin norepinephrine reuptake inhibitor’s venlafaxine and milnacipran did not affect driving ability. Trazodone appears to have dose-related acute effects on driving skills. Acute use of mirtazapine does produce impairments that diminish when given as a nocturnal dose and cannot be seen in healthy subjects when initially given as a low dose or after repeated dosing. Additive effects with alcohol were most pronounced with sedating antidepressants. Most patients definitely benefit from treatment with newer antidepressants with respect to driving skills. Discussion Much more patient studies are needed to elucidate a crucial question: from which antidepressant treatment do patients benefit most with respect to driving performance?

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Shevchenko, Olga. "Regulatory Architecture of Data Processing for Connected and Automated Driving in Europe." International Journal of Law and Public Administration 2, no. 2 (December 1, 2019): 24. http://dx.doi.org/10.11114/ijlpa.v2i2.4594.

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The beginning of the 2020s ought to reflect a steady conclusion of the vast majority of the European Union’s projects with regards to the new era of connectivity and mobility within the European Union dimension. We expect Intelligent Connected Vehicles (ICVs) to step into free circulation within the internal market. Since the operation of the ICVs depends on the number of data processing operations, data processing operations should be precisely determined and framed beforehand. ICVs data operations consist of extraordinarily large volumes and velocity of a data flow which previously existed in traditional relational database systems and could not have been processed within the desired timeframe. Even though the currently adopted database systems are ready to face the new level of data processing, a huge data stream is also faced with complex obstacles and new risks which have never been experienced beforehand.While seeking to ensure safe and secure introduction of a new level of data processing for connectivity and automation at the European Union market, the author precisely examines all potential risks and possibilities of integration into a uniform legal regulation to ensure secured ICVs data processing at all levels. The regulatory framework should document adequate security requirements and defences against ICVs attacks e.g. interference and remote-control interception.

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Yeh, Chung Kee, Yei Po Fang, Kuang Yi Shih, and Maxwell Jiang. "Ergonomic Analysis of the Automotive Head-Up Displayed Information." Advanced Engineering Forum 10 (December 2013): 327–30. http://dx.doi.org/10.4028/www.scientific.net/aef.10.327.

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A vehicles head-up display (HUD) device enables the driver to obtain information, such as driving speed and engine conditions, to ensure safety while keeping the eyes focused on the front windshield. However, no complete and sufficient reference data are available regarding the extent of the drivers acceptability of the information displayed by such a device. Thus, this study aims to examine the drivers visual perception of the vehicular information pattern displayed by the HUD device and its influence on safe driving. The scope of this study includes the scope of the images displayed, quantity of information displayed, distribution and size of information, display brightness in different scenarios, suitable method of information display, correctness of interpretation, and so on. The researcher has built different scenarios for each item studied and analyzed the viewing effect through a questionnaire survey. A database can be built based on such data in the future. This database will contribute to the development of vehicles display systems and verification of similar future studies.

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Sanjaya, Edwin. "Investigate Driving Repurchase Factors in Omnichannel Services." SISFORMA 7, no. 2 (November 23, 2020): 70. http://dx.doi.org/10.24167/sisforma.v7i2.2688.

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Nowadays, lots of companies compete to win their consumers' hearts so they become loyal to the company. Innovation is always done by companies to provide the best service. The omnichannel concept is one of innovation that suitable for this era. Omnichannel is when company can provide seamless experience for their consumer. But, sometimes the reality is not the same as expected before. Even though companies have implemented these omnichannel systems, sometimes they still fail to engage more customers. Therefore, the purpose of this study was to determining factors that influence repurchase intention so that later it can help researchers and also from the managerial side to using existing factors to manage their strategies. The method used in this study was carried out through three steps, namely planning, action, and reporting. Literature search in this study used Google Scholar as a database. Researcher searched articles using keyword repurchase intention, with condition that the articles are complete. This study received ten articles that were investigated by researchers to obtain several influential factors. The finding of this study is there are eighteen factors influence repurchase intention, but further research needs to be done because of different situations, and conditions can produce different results.

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Journal articles on the topic 'Driving database'

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