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Volvo Construction Equipment (VCE) manufactures wheel loaders, articulated haulers, and excavators. By the end of 2030, the company hopes to have reduced the carbon footprint of its machines by 30 %. To increase energy efficiency and productivity, VCE is focused on developing futuristic wheel loaders that are both electric and autonomous. VCE has unveiled its latest autonomous wheel loader prototype called Zeux. This thesis work aims to create a simulation setup that includes a vehicle model of Zeux and a driver model that is optimised for the machine to complete a certain drive/load cycle. This simulation setup will be used to examine the machine’s performance, energy usage, and efficiency and compare it to a conventional machine to determine its advantages and limitations. The new vehicle model was created by modifying a conventional electric machine’s vehicle modeland a new four-wheel steering system was developed. A driver model was developed based on a condition-based decision tree and state machines with unique controllers for each driver input. This complete vehicle-driver simulation set-up has been tunedand optimised with respect to energy efficiency and productivity. The simulation results are then compared to the results of a similar conventional electric machine simulation model. According to the comparison study, the autonomous wheel loader concept has better productivity, lower hydraulic energy consumption as well as lower overall energy consumption compared to the conventional machine. It can complete the drive cycle much more efficiently despite having a similar powertrain and loading unit as the conventional machine., Volvo Construction Equipment (VCE) tillverkar hjullastare, midjestyrda dumprar och grävmaskiner. I slutet av 2030 hoppas företaget ha minskat koldioxidavtrycket för sina maskiner med 30 %. För att öka energieffektiviteten och produktivitet är VCE fokuserade på att utveckla framtida hjullastare som både är elektriska och autonoma. VCE har presenterat sitt senaste autonoma hjullastarprototyp som heter Zeux. Detta examensarbete syftar till att skapa en simuleringsmiljö som innehåller en fordonsmodell av Zeux och en förarmodell som är optimerad för att maskinen ska klara en viss kör-/lastcykel. De framtagna modellerna ska sedan användas för att undersöka maskinens prestanda, energianvändning och effektivitet och jämföra resultaten med en konventionell elektrisk maskin för att fastställa dess fördelar och begränsningar. Den nya fordonsmodellen skapades genom att modifiera en konventionell elektrisk maskins fordonsmodell och ett nytt fyrhjulsstyrningssystem utvecklades. En förarmodell utvecklades baserad på ett tillståndsbaserat beslutsträd och tillståndsmaskiner med unika regulatorer för varje drivrutin. Den kompletta simuleringsmodellen har justerats och optimerats med avseende på energianvändning och produktivitet. Resultaten jämfördes sedan med simuleringsresultat av en liknande konventionell elektrisk hjullastare. Enligt jämförelsestudien, har konceptet med autonoma hjullastare bättre produktivitet, lägre hydrauliskenergiförbrukning samt lägre total energiförbrukning jämfört med den konventionella maskinen. Den kan slutföra körcykeln mycket mer effektivt samtidigt trots att den har en liknande drivlina och lastenhet som den konventionell maskin.

Autonomous Vehicles (AVs) will share the roads with humans, where they will regularly interact with different participating agents such as human-driven vehicles, cyclists, pedestrians, etc. Scenario-based testing is a simulation-based AV testing process where many possible scenarios can be tested inside a simulator to verify that an AV interacts appropriately in corresponding scenarios. According to the current approaches, the participating vehicles in those scenarios are modeled using static, predetermined, time-stamped trajectory information, which fails to obtain the behavioral variability of human drivers. Moreover, such a modeling approach limits the usability of the scenario with continuous software updates of the AVs, as surrounding vehicles remain static while the AVs behavior changes due to system updates. To address this issue, we created CogMod, a cognitive theory-inspired human driver behavior model built on a cognitive framework that integrates two complimentary cognitive architectures, QN-MHP and ACT-R, to reflect human cognition while driving. Contrary to most models, where control is directly based on observed variables, the control actions of CogMod agents rely on a temporally persistent internal representation. This internal representation results from a novel gaze mechanism that enables the CogMod driver to have a selective update of the surrounding environment. The model can simulate human drivers' perceptive and cognitive limitations and thereby capture human driving variability. We put our model through two different simulations to test its ability to generate variable driving behavior. In the first simulation, we explored the influence of decision-making latency, a consequence of variable cognitive processing capacity. This variation, simulated by our model and facilitated by the hybrid cognitive architecture, was evaluated based on the distribution of stopping distances under differing cognitive processing capacities. In the second simulation

This paper aims to find a mathematical justification for the non-linear steady state steering haptic response as a function of driver arm posture. Experiments show that different arm postures, that is, same hands location on the steering wheel but at different initial steering angles, result in a change in maximum driver arm stiffness. This implies the need for different steering torque response as a function of steering angle, which is under investigation. A quasi-static musculoskeletal driver model considering elbow and shoulder joints is developed for posture analysis. The torque acting in the shoulder joint is higher than in the elbow. The relationship between the joint torque and joint angle is linear in the shoulder, whereas the non-linearity occurs in the elbow joint. The simulation results qualitatively indicate a similar pattern as compared to the experimental muscle activity results. Due to increasing muscle non-linearity at high steering angles, the arm stiffness decreases and then the hypothesis suggests that the effective steering stiffness is intentionally reduced for a consistent on-center haptic response., Intelligent Vehicles

The exponential growth of car generated data, the increased connectivity, and the advances in artificial intelligence (AI), enable novel mobility applications. This dissertation focuses on two use-cases of driving data, namely distraction detection and driver identification (ID). Low and medium-income countries account for 93% of traffic deaths; moreover, a major contributing factor to road crashes is distracted driving. Motivated by this, the first part of this thesis explores the possibility of an easy-to-deploy solution to distracted driving detection. Most of the related work uses sophisticated sensors or cameras, which raises privacy concerns and increases the cost. Therefore a machine learning (ML) approach is proposed that only uses signals from the CAN-bus and the inertial measurement unit (IMU). It is then evaluated against a hand-annotated dataset of 13 drivers and delivers reasonable accuracy. This approach is limited in detecting short-term distractions but demonstrates that a viable solution is possible. In the second part, the focus is on the effective identification of drivers using their driving behavior. The aim is to address the shortcomings of the state-of-the-art methods. First, a driver ID mechanism based on discriminative classifiers is used to find a set of suitable signals and features. It uses five signals from the CAN-bus, with hand-engineered features, which is an improvement from current state-of-the-art that mainly focused on external sensors. The second approach is based on Gaussian mixture models (GMMs), although it uses two signals and fewer features, it shows improved accuracy. In this system, the enrollment of a new driver does not require retraining of the models, which was a limitation in the previous approach. In order to reduce the amount of training data a Triplet network is used to train a deep neural network (DNN) that learns to discriminate drivers. The training of the DNN does not require any driving data from the target se

For this project, a Software-In-the-Loop (SIL) simulation tool is used at Scania (“VTAB” – Virtual Truck and Bus), which simulates the submodels of the mechanical vehicle components together with the real control units. The simulation tool contains the following submodels: Engine model, Drivetrain model, Drive cycle model, Restbus model, and Driver model. The simulated human driver submodel in the restbus model outputs two pedal control signals to the control unit, namely the gas and brake pedals. With these two pedal signals, the control unit decides the modes of mechanical vehicle components. This driver model needs to be reworked to obtain a better velocity following performance. Two controllers, fuzzy PI anti-windup and backward calculation, are implemented in the driver model and compared by the velocity tracking accuracy and the pedal switching frequency. In the comparison and analysis section, two different cycles and two weights of payload are simulated. The simulation results demonstrate that both controllers can improve the driver model’s velocity tracing accuracy. Further, the fuzzy PI anti-windup controller is better when considering pedal signals fluctuation frequency and implementation complexity., För detta projekt används ett simuleringsverktyg Software-In-the-Loop (SIL) på Scania (“VTAB” - Virtual Truck and Bus), vilket simulerar submodellerna för de mekaniska fordonskomponenterna tillsammans med de verkliga styrenheterna. Simuleringsverktyget innehåller följande submodeller: Motormodell, Drivmotormodell, Drivcykelmodell, Restbusmodell och Drivermodell. Den simulerade submodellen för mänsklig förare i restbussmodellen kommer att sända två pedalsstyrsignaler till styrenheten, nämligen gas och broms. Med dessa två pedalsignaler kan styrenheten avgöra lägen av mekaniska fordonskomponenter. Denna drivrutinmodell måste omarbetas för att få en bättre hastighetsspårnings presentationsförmåga. Två styrenheter, fuzzy PI anti-windup och bakåtberäkning, implementeras i förarmodell och jämförs respektive med hastighetsspårningsnoggrannhet och pedalväxelfrekvens. I jämförelseoch analysavsnittet simuleras två olika cyklar och två nyttolast. Simuleringsresultaten visar att båda kontrollerna kan förbättra förarmodellens hastighetsspårningskapacitet. Vidare är fuzzy PI-anti-windup-kontroller bättre när man tar hänsyn till pedalsignalernas fluktueringsfrekvens och implementeringskomplexitet

This paper proposes an estimation method for a driver's drowsiness that uses an identified driver model. In the previous report, it was shown that a driver's involuntary response to a car's motion can be extracted by pre-filtering and provided to the real-time identification method. The identified driver model contains meaningful parameters related to the driver's characteristics. In the present research, the correlation between the identified driver model and drowsiness was investigated using the results of an actual driving test. Then, the drowsiness-estimation function was constructed which evaluated the delay, gain, uncertainties, and time variation of the steering behaviour of the identified driver model. The driving test showed that the proposed method had sufficient precision to provide a warning of inattentive driving. © 2015 The Author(s). Published by Taylor & Francis.

A failure-sensitive driver model has been developed in the research study presented in this paper. The model is based on measurements of human responses to dierent failure conditions inuencing the vehicle directional stability in a moving-base driving simulator. The measurements were made in a previous experimental study where test subjects were exposed to three sudden failure conditions that required adequate corrective measures to maintain the vehicle control and regain the planned trajectory. A common driver model and a failure-sensitive driver model have been compared, and results for the latter agree well with the measured data. The proposed failure-sensitive driver model is capable of maintaining the vehicle control and regaining the planned trajectory similarly to the way in which humans achieved this during a wheel hub motor failure in one of the rear wheels., QC 20150520

The rate at which the electrical systems in vehicles such as trucks and buses ad-vance is increased due to technical improvements. This requires continuous de-velopment and advanced testing of these products. At Scania, which is a producerof trucks and buses, they have a simulator to test the electrical systems in thesevehicles. A complete vehicle is created in the simulator where the psychical elec-tronics are tested and the rest of the signals are simulated. One of the simulatedcomponents is a driver algorithm which is used to control the vehicle. This driveralgorithm is today an integrated part of the simulator, which makes it difficult todo maintenance and changes on the driver algorithm. Therefore they want tocreate an external driver algorithm, i.e. a driver algorithm that is not part ofthe simulator. During this thesis work this driver algorithm will be created andtested.Firstly, a simplified vehicle model was created where it is possible to test thedriver algorithm. This was to make the implementation of the driver algorithmeasier, because the vehicle simulator is a narrow resource and to do all tests therewould be difficult. Two different approaches were tested, firstly, a model consis-tent of submodels and secondly, a time varying linear model. The one that gavethe best result was the driver algorithm with the time varying linear model, andthis was used for the rest of the thesis work.The driver algorithm can control the position of the gas and the brake pedalsto make the vehicle drive at a desired velocity. To make the driver follow thedesired velocity, a controller is used in the driver algorithm. Two different con-troller were tested in this master thesis, first a simple PID-controller and then amore advanced MPC. In addition to the ability to follow a reference the driveralgorithm is also able to behave as a human driver would, the driver algorithmdo not for example use both the pedals at the same time.Finally, both the driver algorithms will be compar

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, 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.

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.

A flexible and intuitive non-linear driver model is proposed, which allows setting of physically relevant parameters for representation of both typical high and typical low skill drivers in a path tracking scenario with constant speed. The model is equipped with a relatively simple internal vehicle model and is divided into three levels of driving skill: perceptual, anticipatory and interpretational skill; decisional skill; and execution skill. Validation of the model is performed using the results from moving base driving simulator tests with the double lane change scenario described in ISO 3888-1:1999. The parameter sets used for the model configuration are selected based on physical relevance to the model and optimisation is carried out with a Nelder-Mead implementation, showing that the model is able to resemble the characteristics of the driver types in the scenario for 70 km/h, and with adjustments being able to represent drivers at other speeds., QC 20120615

“If touching is not a single perception but a plural, then its objects are a plurality, too.” (Aristotle) For investigating steering feel, a very important part is how to measure what people feel. The hypothesis in the present research work is that steering feel, as perceived by human beings, can be allotted in dimensions. The steering feel of a certain vehicle can then be seen as a point in a space with multiple orthogonal dimensions. The aim is to find the dimensions that people use to perceive and describe steering feel, in order to define this non-instrumental space. This article describes two modes of evaluation, a manual mode anda mathematical mode using the statistical method of multidimensional scaling. Applying these modes, it has been possible to extract nine dimensions describing the steering feel of road vehicles., QC 20101220

This report documents work conducted in order to support the development of a driver model. This work consisted of (i) a review of driver models for identifying the possibility to add some functionalities to the current model based on existing models and (ii) the data collection and analysis in order to describe the mechanism of distraction and potentially offer some quantification of its effect on drivers’ behavior and performance.

A stochastic threat assessment algorithm for general road scenes is presented. Vehicles behave in a manner which includes a desire to follow their intended paths comfortably and to avoid colliding with other objects. In particular, this can be used to detect indirect threats from objects that are not on a direct collision course, but may be forced into a collision course by the traffic situation. An example is when a vehicle has to swerve to avoid an obstacle and because of that the vehicle itself becomes a threat to another vehicle. The vehicles are on a direct collision course from the beginning, but the situation still poses a threat because of the obstacle. Control inputs of other vehicles are modelled as stochastic variables and the resulting statistical expressions are solved using Monte Carlo sampling. In any Monte Carlo method there is always a trade-off between accuracy, i.e., number of samples, and computational load. A further contribution of this work is a method to create denser sample sets without increasing computational load.

VIRTUAL PROTOTYPING has been generally adopted in product development in order to minimise the traditional reliance on testing of physical prototypes. It thus constitutes a major step towards solving the conflict of actual increasing development cost and time due to increasing customer demands on one side, and the need to decrease development cost and time due to increasing competition on the other. Particularly challenging for the off-road equipment industry is that its products, working machines, are complex in architecture. Tightly coupled, non-linear sub-systems of different technical domains make prediction and optimisation of the complete system’s dynamic behaviour difficult. Furthermore, in working machines the human operator is essential for the performance of the total system. Properties such as productivity, fuel efficiency, and operability are all not only dependent on inherent machine properties and working place conditions, but also on how the operator uses the machine. This is an aspect that is traditionally neglected in dynamic simulations, because the modelling needs to be extended beyond the technical system. The research presented in this thesis focuses on wheel loaders, which are representative for working machines. The technical system and the influence of the human operator is analysed, and so-called short loading cycles are described in depth. Two approaches to rule-based simulation models of a wheel loader operator are presented and used in simulations. Both operator models control the machine model by means of engine throttle, lift and tilt lever, steering wheel, and brake only – just as a human operator does. Also, only signals that a human operator can sense are used in the models. It is demonstrated that both operator models are able to adapt to basic variations in workplace setup and machine capability. Thus, a “human element” can be introduced into dynamic simulation of working machines, giving more relevant answers with respect to opera, ISRN/Report code: LiU-Tek-Lic 2005:44

Prediction and optimisation of a wheel loader's dynamic behaviour is a challenge due to tightly coupled, non-linear subsystems of different technical domains. Furthermore, a simulation regarding performance, efficiency, and operability cannot be limited to the machine itself, but has to include operator, environment, and work task. This paper presents some results of our approach to an event-driven simulation model of a human operator. Describing the task and the operator model independently of the machine's technical parameters, gives the possibility to change whole sub-system characteristics without compromising the relevance and validity of the simulation.

The accuracy in determined energy consumption and runningtime of trains, by means of computer simulation, is dependent upon the various models used. This thesis aims at developing validated models of running resistance, train and of a generaldriver, all based on full scale testing. A partly new simple methodology for determining running resistance, called by energy coasting method is developed and demonstrated. An error analysis for this methodis performed. Running resistance of high speed train SJ X2000, conventional loco hauled passenger trains and freight trains is systematically parameterised. Influence of speed, number of axles, axle load, track type, train length,and train configuration is studied. A model taking into account the ground boundary layer for determining the influence ofmeasured head and tail wind is developed. Different factors and parameters of a train, that are vital for the accuracy in computed energy consumption and runningtime are identified, analysed and finally synthesized into a train model. Empirical models of the braking and the traction system, including the energy efficiency, are developed for the electrical locomotive of typeSJ Rc4, without energy regeneration. Driver behaviour is studied for freight trains and a couple of driving describing parametersare proposed. An empirical model of freight train driver behaviour is developed from fullscale testing and observations. A computer program, a simulator, is developed in Matlabcode, making use of the determined runningresistance and the developed models of train and driver. The simulator calculates the energy consumption and running time ofa single train. Comparisons between simulations and corresponding measurements are made. Finally, the influence of driving on energy consumption and running time is studied and demonstrated in some examples. The main conclusions are that: The method developed for determining running resistanceis quite simple and accurate. It can be used on any train a, QC 20100526 NR 20140805

Diplomová práce se zabývá vytvořením výpočtového modelu řidiče. V první části je vytvořen přehled modelů řidiče pro podélné a příčné řízení vozidla. Dále jsou popsány jízdní manévry, které by mohly být vybrány pro testování modelu řidiče. V praktické části, je vytvořen výpočtový model řidiče, který mám za úkol sledovat požadovanou dráhu. Výsledný vytvořený model je testován na třech jízdních manévrech – ustáleném zatáčení, vyhýbacím manévru a průjezdu slalomem. Na závěr je tento model testován na průjezdu reálné dráhy. Pro všechny tyto dráhy je provedeno srovnání výsledků a hodnocení úspěšnosti modelu., This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.

Diplomová práce se zabývá vytvořením výpočtového modelu řidiče. V první části je vytvořen přehled modelů řidiče pro podélné a příčné řízení vozidla. Dále jsou popsány jízdní manévry, které by mohly být vybrány pro testování modelu řidiče. V praktické části, je vytvořen výpočtový model řidiče, který mám za úkol sledovat požadovanou dráhu. Výsledný vytvořený model je testován na třech jízdních manévrech – ustáleném zatáčení, vyhýbacím manévru a průjezdu slalomem. Na závěr je tento model testován na průjezdu reálné dráhy. Pro všechny tyto dráhy je provedeno srovnání výsledků a hodnocení úspěšnosti modelu., This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.

Diplomová práce se zabývá vytvořením výpočtového modelu řidiče. V první části je vytvořen přehled modelů řidiče pro podélné a příčné řízení vozidla. Dále jsou popsány jízdní manévry, které by mohly být vybrány pro testování modelu řidiče. V praktické části, je vytvořen výpočtový model řidiče, který mám za úkol sledovat požadovanou dráhu. Výsledný vytvořený model je testován na třech jízdních manévrech – ustáleném zatáčení, vyhýbacím manévru a průjezdu slalomem. Na závěr je tento model testován na průjezdu reálné dráhy. Pro všechny tyto dráhy je provedeno srovnání výsledků a hodnocení úspěšnosti modelu., This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.

Diplomová práce se zabývá vytvořením výpočtového modelu řidiče. V první části je vytvořen přehled modelů řidiče pro podélné a příčné řízení vozidla. Dále jsou popsány jízdní manévry, které by mohly být vybrány pro testování modelu řidiče. V praktické části, je vytvořen výpočtový model řidiče, který mám za úkol sledovat požadovanou dráhu. Výsledný vytvořený model je testován na třech jízdních manévrech – ustáleném zatáčení, vyhýbacím manévru a průjezdu slalomem. Na závěr je tento model testován na průjezdu reálné dráhy. Pro všechny tyto dráhy je provedeno srovnání výsledků a hodnocení úspěšnosti modelu., This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.

Diplomová práce se zabývá vytvořením výpočtového modelu řidiče. V první části je vytvořen přehled modelů řidiče pro podélné a příčné řízení vozidla. Dále jsou popsány jízdní manévry, které by mohly být vybrány pro testování modelu řidiče. V praktické části, je vytvořen výpočtový model řidiče, který mám za úkol sledovat požadovanou dráhu. Výsledný vytvořený model je testován na třech jízdních manévrech – ustáleném zatáčení, vyhýbacím manévru a průjezdu slalomem. Na závěr je tento model testován na průjezdu reálné dráhy. Pro všechny tyto dráhy je provedeno srovnání výsledků a hodnocení úspěšnosti modelu., This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.

Diplomová práce se zabývá vytvořením výpočtového modelu řidiče. V první části je vytvořen přehled modelů řidiče pro podélné a příčné řízení vozidla. Dále jsou popsány jízdní manévry, které by mohly být vybrány pro testování modelu řidiče. V praktické části, je vytvořen výpočtový model řidiče, který mám za úkol sledovat požadovanou dráhu. Výsledný vytvořený model je testován na třech jízdních manévrech – ustáleném zatáčení, vyhýbacím manévru a průjezdu slalomem. Na závěr je tento model testován na průjezdu reálné dráhy. Pro všechny tyto dráhy je provedeno srovnání výsledků a hodnocení úspěšnosti modelu., This diploma thesis deals with the creation of a computation driver model. In the first part, there is an overview on driver models for longitudinal and lateral control. Next, driving maneuvres that could be selected for testing of driver model are described. In the practical part, there is created a computational driver model, whose task is to follow required path. The resulting model is tested on three driving maneuvers - steady turning, moose test and slalom. Finally, this model is tested on the passage of a real track. For all these tracks, a comparison is made and the success of the model is evaluated.