I denna rapport redogörs för en implementering av fusion mellan kortdistanssensorer. Syftet med denna implementering är att erhålla Stop & Go-funktionalitet till den adaptiva farthållaren som idag finns som tillval i Scanias lastbilar. Adaptiv farthållning, ACC, är en funktion som automatiskt anpassar fordonets hastighet ifall detta kommer ikapp ett annat fordon som färdas långsammare än den inställda hastigheten. Scanias system använder sig idag av en långdistanssensor som ser långt men har smalt synfält. Genom att komplettera denna med kortdistanssensorer, som ser kort men brett, kan önskvärt synfält i närområdet för att säkert kunna implementera Stop & Go-funktionen uppnås. Sensorfusion bygger på principen att två eller fler sensorer som ser samma sak ger en mer korrekt bild av verkligheten än en enskild. Fusionen kan genomföras på många olika sätt. I rapporten beskrivs tre metoder övergripligt; Bayesiska nätverk, auktionsalgoritmen samt Gating. I implementeringen används gatingmetoden. Initialt implementeras en målspårningsalgoritm med kalmanfilter. Efter uppdatering av sensormjukvaran väljs dock denna bort eftersom sensorernas interna målspårning då anses som tillräcklig. En modell med sensorfusionen och målselekteringen byggs i Simulink och programmeras sedan in i en hårdvaruenhet. Syftet med detta är att kunna utvärdera funktionen i ett prototypfordon. Inledningsvis rapporteras enbart korrelerade mål från modellen. För mer kontinuerlig målföljning, främst i kurvor, implementeras därefter en algoritm som även tar hänsyn till enskilda sensorers observationer. Dessa accpeteras dock först efter en viss valideringstid eftersom denna information har lägre konfidens än korrelerade mål. Provkörningar visar att målföljningen fungerar väl. En stor svårighet har varit att sålla bort stillastående mål från rörliga, främst i låga farter. Detta eftersom sensorernas hastighetsangivelse har låg precision vilket medför att ett stillastående mål kan rapporteras som ett långsamt rörligt. / In this thesis report an implementation of fusion between short range radars is described. The purpose of this implementation is to obtain Stop & Go functionality for the adaptive cruise control which is offered as an option in today’s Scania trucks. Adaptive cruise control, ACC, is a feature that automatically adapts the vehicle speed if it should catch up to another vehicle moving slower than the desired set speed. For this application Scania today use a long range sensor that has a long but narrow field of view. By complementing this sensor with short range sensors, which have short but wide fields of view, the desired field of view in the short range area can be obtained. This is necessary in order to be able to safely implement the Stop & Go functionality. Sensor fusion is based on the principle that two or more sensors overlooking the same area give a more accurate impression of reality than a single one. The fusion can be conducted in several ways. In the report three different methods are briefly described; Bayesian Networks, the Auction Algorithm and Gating. In the implementation the gating method is applied. Initially a target tracking algorithm using Kalman filter is implemented. However, after software updates in the short range sensors this algorithm is no longer used. This is because the improved tracking made internally by the sensors is considered to be sufficient, hence making an external tracking algorithm redundant. The sensor fusion and the target selection are implemented in a Simulink model which is later programmed into a hardware unit. The purpose of the latter is to be able to evaluate the functionality in a prototype vehicle. Initially, only associated targets are reported from the model. In order to obtain a more continuous target tracking, mainly while driving in curves, observations made only by single sensors are also considered. However, these measurements have lower level of confidence than the associated targets. Therefore these measurements first have to be validated for a certain period of time before they are approved. Test runs indicate that the target tracking works as intended. One major difficulty has been to separate stationary targets from slow moving ones, especially in low speeds. This is due to the fact that the sensors’ speed measurements are fairly inaccurate. Therefore a stationary target could be reported as a slow moving one.

<p>I denna rapport redogörs för en implementering av fusion mellan kortdistanssensorer. Syftet med denna implementering är att erhålla Stop & Go-funktionalitet till den adaptiva farthållaren som idag finns som tillval i Scanias lastbilar. Adaptiv farthållning, ACC, är en funktion som automatiskt anpassar fordonets hastighet ifall detta kommer ikapp ett annat fordon som färdas långsammare än den inställda hastigheten. Scanias system använder sig idag av en långdistanssensor som ser långt men har smalt synfält. Genom att komplettera denna med kortdistanssensorer, som ser kort men brett, kan önskvärt synfält i närområdet för att säkert kunna implementera Stop & Go-funktionen uppnås. Sensorfusion bygger på principen att två eller fler sensorer som ser samma sak ger en mer korrekt bild av verkligheten än en enskild. Fusionen kan genomföras på många olika sätt. I rapporten beskrivs tre metoder övergripligt; Bayesiska nätverk, auktionsalgoritmen samt Gating. I implementeringen används gatingmetoden.</p><p>Initialt implementeras en målspårningsalgoritm med kalmanfilter. Efter uppdatering av sensormjukvaran väljs dock denna bort eftersom sensorernas interna målspårning då anses som tillräcklig. En modell med sensorfusionen och målselekteringen byggs i Simulink och programmeras sedan in i en hårdvaruenhet. Syftet med detta är att kunna utvärdera funktionen i ett prototypfordon. Inledningsvis rapporteras enbart korrelerade mål från modellen. För mer kontinuerlig målföljning, främst i kurvor, implementeras därefter en algoritm som även tar hänsyn till enskilda sensorers observationer. Dessa accpeteras dock först efter en viss valideringstid eftersom denna information har lägre konfidens än korrelerade mål.</p><p>Provkörningar visar att målföljningen fungerar väl. En stor svårighet har varit att sålla bort stillastående mål från rörliga, främst i låga farter. Detta eftersom sensorernas hastighetsangivelse har låg precision vilket medför att ett stillastående mål kan rapporteras som ett långsamt rörligt.</p> / <p>In this thesis report an implementation of fusion between short range radars is described. The purpose of this implementation is to obtain Stop & Go functionality for the adaptive cruise control which is offered as an option in today’s Scania trucks. Adaptive cruise control, ACC, is a feature that automatically adapts the vehicle speed if it should catch up to another vehicle moving slower than the desired set speed. For this application Scania today use a long range sensor that has a long but narrow field of view. By complementing this sensor with short range sensors, which have short but wide fields of view, the desired field of view in the short range area can be obtained. This is necessary in order to be able to safely implement the Stop & Go functionality. Sensor fusion is based on the principle that two or more sensors overlooking the same area give a more accurate impression of reality than a single one. The fusion can be conducted in several ways. In the report three different methods are briefly described; Bayesian Networks, the Auction Algorithm and Gating. In the implementation the gating method is applied.</p><p>Initially a target tracking algorithm using Kalman filter is implemented. However, after software updates in the short range sensors this algorithm is no longer used. This is because the improved tracking made internally by the sensors is considered to be sufficient, hence making an external tracking algorithm redundant. The sensor fusion and the target selection are implemented in a Simulink model which is later programmed into a hardware unit. The purpose of the latter is to be able to evaluate the functionality in a prototype vehicle. Initially, only associated targets are reported from the model. In order to obtain a more continuous target tracking, mainly while driving in curves, observations made only by single sensors are also considered. However, these measurements have lower level of confidence than the associated targets. Therefore these measurements first have to be validated for a certain period of time before they are approved.</p><p>Test runs indicate that the target tracking works as intended. One major difficulty has been to separate stationary targets from slow moving ones, especially in low speeds. This is due to the fact that the sensors’ speed measurements are fairly inaccurate. Therefore a stationary target could be reported as a slow moving one.</p>

The aim of this diploma thesis is to specify the displacement and deformations of the railway tracks axis on bridge structures in cities of Zábřeh na Moravě and Břeclav. The bridge structures covered have great dilatation distances, therefore it is necessary to monitor the movements of the railway tracks depending on climatic conditions. GPS methods have been used throughout the whole measurement and data processing. The result includes also a comparison of GPS and conventional methods. The result of the thesis is a detection of proven displacements. One of the goals of the thesis is the comparison of the displacements and the accuracy using the method chosen by author of the thesis and using classical geodetic methods.

<p>The situation when driving in dense traffic and at low speeds is called Stop and Go. A controller for automatic following of the car in front could under these conditions reduce the driver's workload and keep a safety distance to the preceding vehicle through different choices of gear and engine torque. The aim of this thesis is to develop such a controller, with an additional focus on lowering the fuel consumption. With help of GPS, 3D-maps and sensors information about the slope of the road and the preceding vehicle can be obtained. Using this information the controller is able to predict future possible control actions and an optimization algorithm can then find the best inputs with respect to some criteria. The control method used is Model Predictive Control (MPC) and as the name indicate a model of the control object is required for the prediction. To find the optimal sequence of inputs, the optimization method Dynamic Programming choose the one which lead to the lowest fuel consumption and satisfactory following. Simulations have been made using a reference trajectory which was measured in a real traffic jam. The simulations show that it is possible to follow the preceding vehicle in a good way and at the same time reduce the fuel consumption with approximately 3 %.</p>

<p>The department REI/EP at DaimlerChrysler Research and Technology and the Laboratory for Efficient Energy Systems at Trier University of Applied Science, are developing control functions and fuel optimal strategies for low speed conditions. The goal of this thesis project was to further develop the fuel optimal operating strategies, and implement them into a test vehicle equipped with a dSPACE environment. This was accomplished by making optimal reference signals using dynamic programming. Optimal, in this case, means signals that results in low fuel consumption, comfortable driving, and a proper distance to the preceding vehicle. These reference signals for the velocity and distance are used by an MPC controller (Model Predictive Control) to control the car. In every situation a suitable reference path is chosen, depending on the velocities of both vehicles, and the distance. The controller was able to follow another vehicle in a proper way. The distance was kept, the driving was pleasant, and it also seems like it is possible to save fuel. When accepting some deviations in distance to the preceding car, a fuel reduction of 8 % compared to the car in front can be achieved.</p>

The situation when driving in dense traffic and at low speeds is called Stop and Go. A controller for automatic following of the car in front could under these conditions reduce the driver's workload and keep a safety distance to the preceding vehicle through different choices of gear and engine torque. The aim of this thesis is to develop such a controller, with an additional focus on lowering the fuel consumption. With help of GPS, 3D-maps and sensors information about the slope of the road and the preceding vehicle can be obtained. Using this information the controller is able to predict future possible control actions and an optimization algorithm can then find the best inputs with respect to some criteria. The control method used is Model Predictive Control (MPC) and as the name indicate a model of the control object is required for the prediction. To find the optimal sequence of inputs, the optimization method Dynamic Programming choose the one which lead to the lowest fuel consumption and satisfactory following. Simulations have been made using a reference trajectory which was measured in a real traffic jam. The simulations show that it is possible to follow the preceding vehicle in a good way and at the same time reduce the fuel consumption with approximately 3 %.

The department REI/EP at DaimlerChrysler Research and Technology and the Laboratory for Efficient Energy Systems at Trier University of Applied Science, are developing control functions and fuel optimal strategies for low speed conditions. The goal of this thesis project was to further develop the fuel optimal operating strategies, and implement them into a test vehicle equipped with a dSPACE environment. This was accomplished by making optimal reference signals using dynamic programming. Optimal, in this case, means signals that results in low fuel consumption, comfortable driving, and a proper distance to the preceding vehicle. These reference signals for the velocity and distance are used by an MPC controller (Model Predictive Control) to control the car. In every situation a suitable reference path is chosen, depending on the velocities of both vehicles, and the distance. The controller was able to follow another vehicle in a proper way. The distance was kept, the driving was pleasant, and it also seems like it is possible to save fuel. When accepting some deviations in distance to the preceding car, a fuel reduction of 8 % compared to the car in front can be achieved.

Traffic oscillations, or simply stop-and-go waves, are a common phenomenon arising in congested traffic but still not well understood. This phenomenon causes broad adverse impacts to safety risk, fuel efficiency and greenhouse emission. To eliminate or reduce those impacts, understanding the cause and propagation mechanism is essential. This dissertation studied driving behavior in traffic oscillations with the objective to uncover the formation and propagation mechanism of traffic oscillations. This study establishes a behavioral car-following model, the Asymmetric Behavioral model, based on empirical trajectory data that is able to reproduce the spontaneous formation and ensuing propagation of traffic oscillations in congested traffic. By analyzing individual drivers' car-following behavior throughout oscillation cycles it is found that this behavior is consistent across drivers and can be captured by a simple model. The statistical analysis of the model's parameters reveals that driver' behavior during oscillation (i.e., reaction to oscillation) is strongly correlated with driver behavior before oscillations and it varies with the development stage of the oscillation. Simulation of the model shows that it is able to produce characteristics of traffic oscillations consistently with empirical observations. This study also unveils the generation mechanism of the traffic hysteresis phenomenon arising in traffic oscillations using the Asymmetric Behavioral model. It is found that the occurrence of traffic hysteresis is closely correlated with driver behavior when experiencing traffic oscillations. In the growth and fully-developed stage of traffic oscillations, drivers behave differently, which results in different distribution of hysteresis patterns. This research makes it possible to unveil new management and control strategies of traffic oscillations to improve traffic operation and to quantify the environmental and safety impacts of traffic oscillations. For example, it can be used to estimate the increase of greenhouse emission and decrease of fuel efficiency imposed by traffic oscillations. It can also be used to study the increase of accident rate.

We elaborate upon the Herty-Illner macroscopic traffic models which include special non-local forces. The first chapter presents these in relation to the traffic models of Aw-Rascle and Zhang, arguing that non-local forces are necessary for a realistic description of traffic. The second chapter considers travelling wave solutions for the Herty-Illner macroscopic models. The travelling wave ansatz for the braking scenario reveals a curiously implicit nonlinear functional differential equation, the jam equation, whose unknown is, at least to conventional tools, inextricably self-argumentative! Observing that analytic solution methods fail for the jam equation yet succeed for equations with similar coefficients raises a challenging problem of pure and applied mathematical interest. An unjam equation analogous to the jam equation explored by Illner and McGregor is derived. The third chapter outlines refinements for the Herty-Illner models. Numerics allow exploration of the refined model dynamics in a variety of realistic traffic situations, leading to a discussion of the broadened applicability conferred by the refinements: ultimately the prediction of stop-and-go waves. The conclusion asserts that all of the above contribute knowledge pertinent to traffic control for reduced congestion and ameliorated vehicular flow. / Graduate

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