Relevant bibliographies by topics / Model of driver

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Model of driver'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Model of driver"

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Jiang, Haobin, Huan Tian, and Yiding Hua. "Model predictive driver model considering the steering characteristics of the skilled drivers." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401982933. http://dx.doi.org/10.1177/1687814019829337.

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First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers’ steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a β-spline curve.
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Tan, Yun Long, and Hong Fei Jia. "Establishment and Validation of Mainline Driver Type Model at Expressway-Ramp Merging Area." Applied Mechanics and Materials 409-410 (September 2013): 1392–97. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1392.

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The driver characteristic is an important factor that affects driver behaviors, however, the existing driver behavior models little consider the influence of driver own characteristic differences on the driver behaviors. As the driver mental and physical behaviors in the process of driving are uncertainty and ambiguity, the mainline vehicles at expressway-ramp merging area are selected as research object, and the fuzzy clustering theory is introduced. In order to describe the mainline drivers characteristics accurately, the mainline vehicle acceleration, the relative speed of the current mainline vehicle to the all mainline vehicles and the lag gap of the mainline vehicle are selected to cluster by the fuzzy clustering method, and the driver type distribution model is built by K-S test method. Then, the driver type distribution data as a key parameter is incorporated into the expressway merging model, in order to represent the effect of driver characteristic on drive behavior. Finally, the microscopic traffic simulation system MTSS is taken as the simulation plat to build simulation model and validate the built mainline driver type model, the output results from the simulation system are compared with the field data, the satisfactory results indicate that the built driver type model can be used to describe the impact of driver type on driving behavior.
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Feng, Yuxiang, Pejman Iravani, and Chris Brace. "A Fuzzy Logic-Based Approach for Humanized Driver Modelling." Journal of Advanced Transportation 2021 (June 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/4413505.

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All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.
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Braghin, F., F. Cheli, S. Melzi, and E. Sabbioni. "Race driver model." Computers & Structures 86, no. 13-14 (July 2008): 1503–16. http://dx.doi.org/10.1016/j.compstruc.2007.04.028.

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Chen, Liang-Kuang, and A. Galip Ulsoy. "Identification of a Driver Steering Model, and Model Uncertainty, From Driving Simulator Data." Journal of Dynamic Systems, Measurement, and Control 123, no. 4 (January 17, 2001): 623–29. http://dx.doi.org/10.1115/1.1409554.

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For active safety systems that function while the driver is still in the control loop, driver uncertainty can affect system performance significantly. In this paper, an approach to obtain both the driver model and its uncertainty from driving simulator data is presented. The structured uncertainty is used to represent the driver’s time-varying behavior, and the unstructured uncertainty for unmodeled dynamics. The uncertainty models can represent both the uncertainty within one driver and the uncertainty across multiple drivers. The structured uncertainty suggests that an estimation and adaptation scheme might be applicable for the design of controllers for active safety systems.
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Mubasher, Mian Muhammad, and Syed Waqar Ul Qounain Jaffry. "Incorporation of the Driver’s Personality Profile in an Agent Model." PROMET - Traffic&Transportation 27, no. 6 (December 21, 2015): 505–14. http://dx.doi.org/10.7307/ptt.v27i6.1675.

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Urban traffic flow is a complex system. Behavior of an individual driver can have butterfly effect which can become root cause of an emergent phenomenon such as congestion or accident. Interaction of drivers with each other and the surrounding environment forms the dynamics of traffic flow. Hence global effects of traffic flow depend upon the behavior of each individual driver. Due to several applications of driver models in serious games, urban traffic planning and simulations, study of a realistic driver model is important. Hhence cognitive models of a driver agent are required. In order to address this challenge concepts from cognitive science and psychology are employed to design a computational model of driver cognition which is capable of incorporating law abidance and social norms using big five personality profile.
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Wen, Huiying, Zuogan Tang, Yuchen Zeng, and Kexiong Zhang. "A Comprehensive Analysis for the Heterogeneous Effects on Driver Injury Severity in Single-Vehicle Passenger Car and SUV Rollover Crashes." Journal of Advanced Transportation 2020 (January 13, 2020): 1–13. http://dx.doi.org/10.1155/2020/1273605.

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In road traffic crashes, although rollover crashes account for a relatively low proportion, those result in a high fatality rate. The present study performed random parameters ordered logit models to examine risk factors as well as their heterogeneous effects on driver injury severity in single-vehicle passenger car and SUV rollover crashes. Crash data for the empirical analysis were extracted from Texas Crash Record Information System (CRIS) database during the year 2016. Model estimation results show that six variables (male drivers, drivers’ age, airbag deployment, failure to drive in single lane, speed limit, and rural area) were found to produce normally distributed parameters in passenger car model, while nine parameters (male drivers, safety belt use, airbag deployment, drug or alcohol use, failure to drive in single lane, improper evasive action, vehicle model year, friday, and rural area) in SUV model were found to be normally distributed. Several other factors with fixed parameters were found to be associated with driver injury severity in single-vehicle passenger car or SUV rollover crashes, most notably: ejection or partial ejection, turning left, intersection, August, adverse weather conditions, and night with light. These variables were significant in both models; most variables have stronger effects on nonincapacitating injury and serious injury outcomes in SUV than in passenger car rollover crashes. These findings provide a deep insight into causality nature and factor involved in driver injury severity in single-vehicle passenger car and SUV rollover crashes and are also helpful for transport agencies to determine appropriate countermeasures aimed at mitigating injuries sustained by drivers in single-vehicle rollover crashes.
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McGordon, A., J. E. W. Poxon, C. Cheng, R. P. Jones, and P. A. Jennings. "Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 225, no. 11 (July 20, 2011): 1518–30. http://dx.doi.org/10.1177/0954407011409116.

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The real-world fuel economy of vehicles is becoming increasingly important to manufacturers and customers. One of the major influences in this is driver behaviour, but it is difficult to study in a controlled and repeatable manner. An assessment of driver models for studying real-world driver behaviour has been carried out. It has been found that none of the currently existing driver models has sufficient fidelity for studying the effects of real-world driver behaviour on the fuel economy of the individual vehicle. A decision-making process has been proposed which allows a driver model with a range of driving tasks to be developed. This paper reports the initial results of a driver model as applied to the conceptually straightforward scenario of high-speed cruising. Data for the driver model have been obtained through real-world data logging. It has been shown that the simulation driver model can provide a good representation of real-world driving behaviour in terms of the vehicle speed, and this is compared with a number of logged driver speed traces. A comparison of the modelled fuel economy for logged and driver model real-world drivers shows good agreement.
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Derbel, Oussama, Tamás Péter, Hossni Zebiri, Benjamin Mourllion, and Michel Basset. "Modified Intelligent Driver Model." Periodica Polytechnica Transportation Engineering 40, no. 2 (2012): 53. http://dx.doi.org/10.3311/pp.tr.2012-2.02.

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Kruszewski, Mikołaj, and Mirosław Nader. "Analysis of the attention distraction of inexperienced drivers using a fuzzy model – research results." WUT Journal of Transportation Engineering 125 (June 1, 2019): 53–62. http://dx.doi.org/10.5604/01.3001.0013.6570.

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Limiting the number and consequences of the traffic accidents is one of the most important goals of the EU policy for the road transport. Despite significant efforts in this area, the targets set for the 2010-2020 decade are unlikely to be achieved. This may be due to, inter alia, the increasing importance of the driver attention distraction as a factor contributing to their occurrence. In order to limit the effects of distraction, attempts are made to develop a method to detect such a state of a driver. The distraction of the driver affects the way he drives the vehicle. The authors in their earlier work conducted a research aimed at developing model for detecting states of distraction of the driver's attention, based on a change in the method of vehicle steering. The developed model uses fuzzy logic to detect distraction. This paper presents the results of this model's operation on a sample of 72 drivers, including 36 inexperienced drivers who were the main object of the tests.
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Dissertations / Theses on the topic "Model of driver"

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Almén, Marcus. "Driver Model for Mission-Based Driving Cycles." Thesis, Linköpings universitet, Fordonssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140158.

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When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features. For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO. The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.
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Guillen, Pablo Puente, and Irene Gohl. "Forward collision warning based on a driver model to increase drivers’ acceptance." Taylor & Francis, 2019. https://publish.fid-move.qucosa.de/id/qucosa%3A72239.

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Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers’ acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT (“Proactive Safety for Pedestrians and Cyclists”). Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4 s (it takes the cyclist 4 s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6 s (asymptotic value of the model at TTA = 4 s) and the warning outside the CB was at TTC = 1.7 s (below the lower 95% value at TTA = 4 s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey. Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models. Conclusions: Similar to other studies’ results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers.
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Ehsani, Saéd M. "A driver model with supervision aspects." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0007/NQ42821.pdf.

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SANTOS, Luiz José Rodrigues dos. "Generalização do modelo computacional de tráfego veicular IDM (Intelligent Driver Model)." Universidade Federal Rural de Pernambuco, 2008. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5172.

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Submitted by (ana.araujo@ufrpe.br) on 2016-08-03T14:07:18Z No. of bitstreams: 1 Luiz Jose Rodrigues dos Santos.pdf: 1081987 bytes, checksum: 435fc2cb438881b9c3905e16b4b41ed0 (MD5)
Made available in DSpace on 2016-08-03T14:07:42Z (GMT). No. of bitstreams: 1 Luiz Jose Rodrigues dos Santos.pdf: 1081987 bytes, checksum: 435fc2cb438881b9c3905e16b4b41ed0 (MD5) Previous issue date: 2008-02-28
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Urban traffic represents a phenomenon of great socioeconomic importance,whose modeling from the point of view of prevision on the basis of initial conditions, still represents a challenge for modern science. Computational methods (computer simulations) represent a powerful tool for modeling and prediction of a number of effects, where systems of coupled differential equations may be used to simulate different phenomena observed in traffic systems. In particular, a quantity of high importance for maintenance and planning of road systems is the vehicular capacity which can be supported without traffic jams, whose description and prevision is still not well understood. In this work, a generalization of an existing microscopic traffic model, the Intelligent Driver Model (IDM), is proposed by implementing a distribution of desired velocities, where it is shown that vehicle capacity of multiple lane roads can be measured in a rather realistic manner, as a function of model parameters,which may be adjusted to real observations.
O tráfego urbano representa um fenômeno de grande importância sócio econômica, cuja modelagem de ponto de vista de previsão a partir de condições iniciais, ainda representa um desafio para a ciência moderna. Métodos computacionais (simulação computacional) representam uma ferramenta poderosa para modelagem e previsão de diversos efeitos, nos quais sistemas de equações diferenciais acopladas podem simular diversos fenômenos observados no sistema de tráfego. Em particular, uma grandeza de alto impacto para o gerenciamento e planejamento de rodovias é a capacidade veicular que elas podem suportar sem que aconteça o efeito de congestionamento, cuja descrição e previsão ainda não estão bem entendida. Neste trabalho, propõe-se uma generalização de um modelo microscópico computacional existente, o Intelligent Driver Model (IDM), aplicando uma distribuição de velocidades desejadas, onde torna-se possível medir de forma bastante realista a capacidade veicular de rodovias com múltiplas faixas, em função de parâmetros de modelo, que podem ser ajustados às observações reais.
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Qiu, Jie. "Study of driver models forside wind disturbances." Thesis, KTH, Fordonsdynamik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157504.

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As the development of highways, it is quite normal for buses running in a speed around 100km/h. When buses are running in a high speed, they may suffer from the influence of side wind disturbances at anytime. Sometimes, it may result in traffic accidents. Therefore, the study of bus stability under side wind disturbances becomes more and more important. Due to restrictions of real tests, computer simulation can be used to study this subject. The bus side wind response character is reflected through the driver’s manoeuvre , so open-loop analysis is hard to give a comprehensive evaluation of the side wind stability of the bus. Therefore, closed-loop analysis is studied in this thesis. An ADAMS bus model and a side wind force model are developed in this thesis, along with two driver models, the PID control model and the preview curvature model. The driver models are built in Simulink and co-simulation between ADAMS/View and Simulink is conducted. The results of co-simulation show that the two driver models can both control the bus from deviating from the desired course under side wind disturbances. The PID control model is simple and shows a very good control effect. The maximum lateral displacement of the bus by PID control model is just 0.0205m under maximum side wind load 1000N and 2500Nm when preview time is 1.2s, while it is 0.0702m by preview curvature model, however, it is difficult to determine the coefficients Kd, Kp, and Ki in the PID controller. The preview curvature model also shows a good control effect in terms of the maximum lateral displacement and yaw angle of the bus. Comparing these two models, the PID control model is more sensitive to deviations, with quicker response and larger steering input. The bus model system is stable under side wind disturbances. Through driver ’s proper steering manoeuvre, the bus is well controlled. The closed-loop analysis is a good method to study the bus stability under side wind disturbances.
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Jansson, Andreas, and Erik Olsson. "Development of a Driver Model for Vehicle Testing." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-102048.

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The safety requirements for vehicles are today high and they will become more stringent in the future. The car companies test their products every day to ensure that safety requirements are met. These tests are often done by professional drivers. If the car is tested in an everyday traffic situation, a normal experienced driver is desired. A drawback is that a human will eventually learn the manoeuvre he/she is told to do. An artificial driver is therefore to prefer to make the test repeatable. This thesis’ purpose is to develop and implement an artificial driver as a controller in order to follow a predefined trajectory. The driver model’s performance driving a double lane change manoeuvre should be as close to a real driver’s as possible. Data was gathered by inviting people to drive in a simulator. The results from the simulator tests were used to implement three different drivers with different experiences. The gathered data was used to categorize the test drivers into different driver types for each specific velocity by using the vehicle position from thetest results. This thesis studies the driver from a controller’s perspective and it resulted in two implemented controllers for reference tracking. The first approach was a Model Predictive Controller with reference tracking and the other approach was to use a FIR-filter in order to describe the drivers’ characteristics. A vehicle model was implemented in order to do the double lane change manoeuvre in a simulation environment together with the implemented driver model. The results show that the two approaches can be used for reference tracking. The MPC showed good results with the recreation of the test runs that were made by the categorized drivers. The FIR-filter had problems to mimic the drivers’ test runs and their characteristics. The advantage with MPC is its robustness, while the advantages with the FIR-filter are its, in comparison, simplicity in the implementation and the algorithm’s low computational cost. In order to make the FIR-filter more robust, some improvements have to be made. One improvement is to use gain scheduling in order to adjust the filter coefficients depending on thevelocity.
De säkerhetskraven som idag ställs på fordon är höga och det kommer bli mer strikt i framtiden. Bilföretag testar sina bilar varje dag för att se om komponenterna och bilen klarar säkerhetskraven som ställs. Till dessa tester används professionella testförare. I en vardaglig trafiksituation är det önskvärt att en normalt erfaren bilförare utför testen. En mänsklig förare kommer använda sin inlärningsförmåga vid repeterande manöver, vilket inte är önskvärt. En artificiell förare är därför att föredra. Den artificiella föraren ska köra så likt en verklig förare som möjligt vid en "double lane change"- (DLC) manöver. Detta examensarbete har som avsikt att implementera en förare som en regulator för att kunna följa en förutbestämd trajektoria på samma sätt som en verklig förare. I detta examensarbete har "DLC"-manövern studerats. I examensarbetet har insamlad data från testförare använts för att kunna implementera tre olika förartyper med olika erfarenheter. Den insamlade datan användes till att kategorisera testförarna för varje särskild hastighet. Två tillvägagångssätt har gjorts med föraren, en där föraren är en modellbaserad prediktionsregulator med referensignalsföljning (MPC) och en där föraren implementeras som ett ändligt impulssvarsfilter (FIR-filter). En fordonsmodell har implementerats för att en "DLC"-manöver ska kunna testas i en simuleringsmiljö. Resultaten blev att de två metoderna klarade av referensföljningen. MPC:n var bra på att återskapa testförararnas körningar. FIR-filtret hade problem med att härma förarnas körningar och deras karaktäristik. Fördelen med MPC är dessrobusthet och fördelen med FIR-filtret är dess, i jämförelse, simplicitet vid implementering samt den låga beräkningskostnaden för algoritmen. För att göra FIR-filtret mer robust måste förbättringar göras. En förbättring är att använda gain scheduling för att anpassa filterkoefficienterna beroende på hastigheten.
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Hoult, W. "A neuromuscular model for simulating driver steering torque." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604261.

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A linear neuromuscular model was developed and incorporated within a driver/vehicle model. Optimal control was used to minimise metabolic energy and path-following error. Simultaneous feed-forward and feedback operation was observed, with the stretch reflex loop acting to reject disturbances. A trade-off between minimising the feedback error signal and energy consumption exists that has not been previously identified. A non-linear, Huxley/Zahalak-based model of an agonist/antagonist muscle pair connected to a second order load was implemented (the ‘MDM’ model). Mechanistic and energy consumption predictions compare favourably with published data. The model was linearized, to allow incorporation within a linear neuromuscular framework. A suitable model structure was fitted using parametric methods. A novel, linear, energy consumption model was proposed. A parameter study of the MDM model was carried out. Variable natural length behaviour was observed, consistent with real muscle operation. Findings suggested that the stretch reflex gain is not large enough to account for low frequency behaviour observed by some researchers for ‘stochastic disturbance’ type experiments. An optimal controller representing cognitive influence was shown to account for this behaviour. A Box-Jenkins method for identifying intrinsic and reflex dynamics models (on the basis of reflex delay) was developed and validated. The impact of the stretch reflex gain and noise levels on identification success was investigated. Intrinsic and reflex models were identified from eight test subjects’ data. The closed-loop neuromuscular model agreed well with measured data, and was generally consistent with MDM model predictions. Low frequency control action and changes in stretch reflex dynamics were observed, stemming from cognitive influence. Other researchers have failed to account for this.
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Kappattanavar, Abhishek Mallikarjuna. "Model Transformation in context of Driver Assistance System." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-203859.

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In today’s world we see that Embedded Systems forms a major part in the life of a human being. Almost every device today has an electronic chip embedded in it. When it comes to automotive, these electronic devices are multiplying. This has resulted in innovative methods of developing Embedded Systems. Among them, Model Based Development has become very popular and a standard way of developing embedded systems. Now, we can see that most embedded systems, especially the automotive systems, are being developed using Model development tools like Simulink. In the design and development of Driver Assistance System, Model Based Design (MBD) plays an important role from system design and simulation to code generation. Modeling tool Matlab/Simulink is now among the most popular tools. Due to the proprietary nature of Simulink and challenges in requirement elicitation phase the industry is looking towards an open source alternative, such as Scicos. Since, most of the OEMs are still using Simulink, there is a need for interoperability between Simulink and Scicos. The present work proposes metamodels for Simulink and Scicos, and Model transformation using these Metamodels for the inter-operability. In order to develop the model transformation the metamodels for Simulink and Scicos were developed using EMF Ecore. These metamodels conform to OMGs MOF Standards. These metamodels were used in developing the transformation definition using the language QVTo. First a simple model was developed, and transformation rules were applied and verified using it. Then a Simulink subsystem of a cross wind assistance system was subjected to forward transformation. The outputs of the model before transformation and that after transformation were compared. They were found to give the same output as desired. Thus, verifying the transformation definition. An attempt was made to achieve reverse transformation. A subsystem in Scicos was considered for reverse transformation. After subjecting it to transformation, an intermediate model conforming to Simulink metamodel was obtained. This shows that the interoperability between Scicos and Simulink can be achieved.
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Haupt, Juliane. "Be motivated to pay attention! How driver assistance system use experience influences driver motivation to be attentive." Doctoral thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-206704.

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This work provides an in-depth-view of driver motivational aspects when driver assistance Systems (DAS) are considered. Thereby, the role of driver actual experience with DAS use was also identified and highlighted. A central outcome of this thesis is the STADIUM model describing the interplay of motivational factors that determine the engagement in secondary activities while taking actual DAS use experience into account. The role of motives in showing attentive behaviour depending on DAS (the navigation system) could also be underlined. The relevance, enrichment and need of combining qualitative and quantitative approaches when the effects of safety countermeasures on driver behaviour are investigated could also be shown. The results are discussed in terms of hierarchical driver behaviour models, the theory of planned behaviour and its extended versions and the strengths of the introduced studies and limitations. Implications for traffic safety are provided and future research issues are recommended
Diese Arbeit liefert einen gründlichen Einblick, welche Rolle motivationale Aspekte spielen, wenn Fahrerassistenzsysteme (FAS) genutzt werden. Dabei wurde auch die Funktion der tatsächlichen Erfahrung mit FAS identifiziert und hervorgehoben. Ein zentrales Ergebnis dieser Arbeit ist das STADIUM Modell, welches das Zusammenspiel motivationaler Faktoren in Abhängigkeit von der tatsächlichen Erfahrung mit FAS erklärt, die wiederum bestimmen, inwieweit und ob andere Aktivitäten während des Fahrens ausgeführt werden. Außerdem konnte unterstrichen werden, welche Rolle Motive spielen, aufmerksames Verhalten in Abhängigkeit von der Nutzung von FAS (dem Navigationssystem) zu zeigen. Zusätzlich konnte dargestellt werden, wie relevant, bereichernd und nützlich es ist, qualitative und quantitative Methoden zu kombinieren, wenn die Effekte von FAS auf das FahrerInnenverhalten untersucht werden. Die Ergebnisse werden diskutiert indem auf hierarchische Fahrerverhaltensmodelle, auf die Theorie des geplanten Verhaltens und ihre erweiterten Versionen und auf die Stärken und Schwächen der Studien Bezug genommen wird. Es werden Implikationen dargestellt und zukünftige Forschungsfragen und Problemstellungen empfohlen
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Miyajima, Chiyomi, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda, and Fumitada Itakura. "Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification." IEEE, 2007. http://hdl.handle.net/2237/9623.

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Books on the topic "Model of driver"

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Reid, L. D. A computer model of driver response. Downsview, Ont: Institute for Aerospace Studies, 1985.

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Programming the Microsoft Windows Driver Model. 2nd ed. Redmond, Wash: Microsoft Press, 2002.

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Bagnoli, Carlo, Alessia Bravin, Maurizio Massaro, and Alessandra Vignotto. Business Model 4.0. Venice: Edizioni Ca' Foscari, 2018. http://dx.doi.org/10.30687/978-88-6969-286-4.

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The manufacturing digital transformation is changing the industry through the introduction of advanced solutions that allow companies to re-interpret their role along the value chain. The industrial revolution opens up great opportunities for Italian companies, in terms of process efficiency, cost reduction and improvement in productivity, but also in the rethinking of products, new services, and the ability of reaction to market needs. This report examines the possible impact of Industry 4.0 on business models considering technological innovation also as a driver of strategic innovation.
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Rempp, Gerhard, Mark Akermann, Martin Löffler, and Jens Lehmann. Model Driven SOA. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14470-7.

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Aßmann, Uwe, Mehmet Aksit, and Arend Rensink, eds. Model Driven Architecture. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538097.

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Prinz, Andreas, Rick Reed, and Jeanne Reed, eds. SDL 2005: Model Driven. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b137793.

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Lund, Mass Soldal, Bjørnar Solhaug, and Ketil Stølen. Model-Driven Risk Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-12323-8.

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Herden, Sebastian. Model-Driven-Configuration-Management. Wiesbaden: Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-01107-9.

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Beydeda, Sami, Matthias Book, and Volker Gruhn, eds. Model-Driven Software Development. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-28554-7.

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Davis, William D. A sensor-driven fire model. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2001.

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Book chapters on the topic "Model of driver"

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Parnell, Katie J., Neville A. Stanton, and Katherine L. Plant. "Evolution of the PARRC Model of Driver Distraction." In Driver Distraction, 177–94. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor &: CRC Press, 2018. http://dx.doi.org/10.1201/9780429466809-9.

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Khazem, Kareem, and Michael Tautschnig. "smid: A Black-Box Program Driver." In Model Checking Software, 182–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32582-8_12.

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Huffschmid, Jörg. "Finance as Driver of Privatisation." In Privatisation against the European Social Model, 49–60. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230250680_4.

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Xiao, Qiong, Changzhen Hu, and Gangyi Ding. "A Driver Model Based on Emotion." In Lecture Notes in Computer Science, 608–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61833-3_64.

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Kim, Moonzoo, Yunja Choi, Yunho Kim, and Hotae Kim. "Formal Verification of a Flash Memory Device Driver – An Experience Report." In Model Checking Software, 144–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85114-1_12.

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Relangi, Sree Pradeep Kumar, Mutyam Nilesh, Kintali Pavan Kumar, and Anantapalli Naveen. "Full Length Driver Drowsiness Detection Model—Utilising Driver Specific Judging Parameters." In Intelligent Manufacturing and Energy Sustainability, 791–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1616-0_77.

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Cody, Delphine, and Timothy Gordon. "TRB Workshop on Driver Models: A Step Towards a Comprehensive Model of Driving?" In Modelling Driver Behaviour in Automotive Environments, 26–42. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-618-6_2.

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Baumann, M., and J. F. Krems. "Situation Awareness and Driving: A Cognitive Model." In Modelling Driver Behaviour in Automotive Environments, 253–65. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-618-6_14.

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Ito, Masao. "Controllability in ISO 26262 and Driver Model." In Communications in Computer and Information Science, 313–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24647-5_26.

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Rahman, Md Mahmudur, Shuchisnigdha Deb, Daniel Carruth, and Lesley Strawderman. "Using Technology Acceptance Model to Explain Driver Acceptance of Advanced Driver Assistance Systems." In Advances in Human Factors of Transportation, 44–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20503-4_5.

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Conference papers on the topic "Model of driver"

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"Driver model uncertainty." In Proceedings of the 1999 American Control Conference. IEEE, 1999. http://dx.doi.org/10.1109/acc.1999.782920.

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Absil, Neil E., and Jasper Pauwelussen. "Driver Model Library." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642438.

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Dong, Yunwei, Yuanyuan He, Yin Lu, and Hong Ye. "A Model Driven Approach for Device Driver Development." In 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2017. http://dx.doi.org/10.1109/qrs-c.2017.27.

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Nishigaki, Morimichi, and Tetsuro Shirakata. "Driver attention level estimation using driver model identification." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917380.

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Anflor, Carla Tatiana Mota, Tiago Becker, and Alberto Tamagna. "Seat Driver Model Development." In 2004 SAE Brasil Congress and Exhibit. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2004. http://dx.doi.org/10.4271/2004-01-3340.

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Eggert, Julian, Florian Damerow, and Stefan Klingelschmitt. "The Foresighted Driver Model." In 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2015. http://dx.doi.org/10.1109/ivs.2015.7225706.

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Chen, Hui, Guillaume Godet-Bar, Frederic Rousseau, and Frederic Petrot. "Me3D: A model-driven methodology expediting embedded device driver development." In 2011 22nd IEEE International Symposium on Rapid System Prototyping (RSP). IEEE, 2011. http://dx.doi.org/10.1109/rsp.2011.5929992.

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Sohani, Nauman, Geunseob GS Oh, and Xinpeng Wang. "A Data-driven, Falsification-based Model of Human Driver Behavior." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147410.

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Day, Terry D., and L. Daniel Metz. "The Simulation of Driver Inputs Using a Vehicle Driver Model." In SAE 2000 World Congress. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2000. http://dx.doi.org/10.4271/2000-01-1313.

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Liu, Yiting, and Umit Ozguner. "Human Driver Model and Driver Decision Making for Intersection Driving." In 2007 IEEE Intelligent Vehicles Symposium. IEEE, 2007. http://dx.doi.org/10.1109/ivs.2007.4290188.

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Reports on the topic "Model of driver"

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Hallquist, J. O., and R. G. Whirley. A material model driver for DYNA3D. Office of Scientific and Technical Information (OSTI), February 1990. http://dx.doi.org/10.2172/6529731.

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Heimerl, Joseph M. Driver Performance Model: 1. Conceptual Framework. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada397331.

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Boer, Erwin R., Nicholas J. Ward, Michael P. Manser, and Nobuyuki Kuge. Driver-Model-Based Assessment of Behavioral Adaptation. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0307.

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Meier, W. R. A new heavy-ion-beam driver model for the SAFIRE code. Office of Scientific and Technical Information (OSTI), March 1987. http://dx.doi.org/10.2172/6395111.

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Kuge, Nobuyuki, Dario Salvucci, and Tomohiro Yamamura. Research on a Lange Change Intent Inference Based on Driver Model With Cognitive Scientific Validity. Warrendale, PA: SAE International, September 2005. http://dx.doi.org/10.4271/2005-08-0570.

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Althoff, J. L., and S. Singh. Integrated Information Support System (IISS). Volume 5. Common Data Model Subsystem. Part 25. NDML Precompiler Generate Request Processor Driver Product Specification. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada182013.

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Sekuler, Robert. Model-Driven Study of Visual Memory. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada435772.

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Thomassen, K. I. Circuit Model for Gun Driven Spheromaks. Office of Scientific and Technical Information (OSTI), July 2000. http://dx.doi.org/10.2172/15013538.

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Barkow, Ingo, ed. Developing a Model-Driven DDI Specification. Inter-university Consortium for Political and Social Research (ICPSR), 2013. http://dx.doi.org/10.3886/ddiworkingpaper04.

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Davis, William D., and Glenn P. Forney. A sensor-driven fire model, version 1.1. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6705.

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