Traffic signals typically produce vehicle stops and thus increase vehicle fuel consumption levels. Vehicle stops produced by traffic signals, decrease vehicle fuel economy on arterial roads making it significantly lower than that on freeways. Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems can improve vehicle fuel efficiency by receiving Signal Phasing and Timing (SPaT) data form downstream signalized intersections via vehicle-to-infrastructure communication. The algorithm that was developed in an earlier study provides advisory speed recommendations to drivers to reduce vehicle fuel consumption levels in the vicinity of traffic signalized intersections. The research presented in this thesis enhances the algorithm by adding a queue length estimation component and incorporates the algorithm in the INTEGRATION microscopic traffic simulation software to test the system under varying conditions. The enhanced Eco-CACC algorithm is then tested in a simulation environment considering different levels of connected vehicle (CV) market penetration levels. The simulation analysis demonstrates that the algorithm is able to reduce the vehicle fuel consumption level by as high as 40%. Moreover, the overall benefits of the proposed algorithm is evaluated for different intersection configurations and CV market penetration rates (MPRs). The results demonstrate that for single lane approaches, the algorithm can reduce the overall fuel consumption levels and that higher MPRs result in larger savings. While for multilane approaches, lower MPRs produce negative impacts on fuel efficiency; only when MPRs are greater than 30%, can the algorithm work effectively in reducing fuel consumption levels. Subsequently a sensitivity analysis is conducted. The sensitivity analysis demonstrates that higher market penetration rates of Eco-CACC enabled vehicles can improve the environmental benefits of the algorithm, and the overall savings in fuel consumption are as high as 19% when all vehicles are equipped with the system. While, on multi-lane approaches, the algorithm has negative impacts on fuel consumption levels when the market penetration rate is lower than 30 percent. The analysis also indicates that the length of control segments, the SPaT plan, and the traffic demand levels affect the algorithm performance significantly. The study further demonstrates that the algorithm has negative impacts on fuel consumption levels when the network is over-saturated. / Master of Science

Stop-and-Go Adaptive Cruise Control (ACC+) is an extension of Adaptive Cruise Control (ACC) that works at low speed as well as normal highway speeds to regulate the speed of the vehicle relative to the vehicle it is following. In this thesis, we design an ACC+ controller for a scale model electric vehicle that ensures the robust performance of the system under various models of uncertainty. We capture the operation of the hybrid system via a state-chart model that performs mode switching between different digital controllers with additional decision logic to guarantee the collision freedom of the system under normal operation. We apply different controller design methods such as Linear Quadratic Regulator (LQR) and H-infinity and perform multiple simulation runs in MATLAB/Simulink to validate the performance of the proposed designs. We compare the practicality of our design with existing formally verified ACC designs from the literature. The comparisons show that the other formally verified designs exhibit unacceptable behaviour in the form of mode thrashing that produces excessive acceleration and deceleration of the vehicle. While simulations provide some assurance of safe operation of the system design, they do not guarantee system safety under all possible cases. To increase confidence in the system, we use Differential Dynamic Logic (dL) to formally state environmental assumptions and prove safety goals, including collision freedom. The verification is done in two stages. First, we identify the invariant required to ensure the safe operation of the system and we formally verify that the invariant preserves the safety property of any system with similar dynamics. This procedure provides a high level abstraction of a class of safe solutions for ACC+ system designs. Second, we show that our ACC+ system design is a refinement of the abstract model. The safety of the closed loop ACC+ system is proven by verifying bounds on the system variables using the KeYmaera verification tool for hybrid systems. The thesis demonstrates how practical ACC+ controller designs optimized for fuel economy, passenger comfort, etc., can be verified by showing that they are a refinement of the abstract high level design. / Thesis / Master of Applied Science (MASc)

M.Ing. (Electrical and Electronic Engineering) / Adaptive Cruise Control (ACC) is proposed to assist drivers tedious manual acceleration or braking of the vehicle, as well as with maintaining a safe headway time gap. This thesis proposes, simulates, and implements a vision-based ACC system which uses a single camera to obtain the clearance distance between the preceding vehicle and the ACC vehicle. A three-step vehicle detection framework is used to obtain the position of the lead vehicle in the image. The vehicle coordinates are used in conjunction with the lane width at that point to estimate the longitudinal clearance range. A Kalman filter filters this range signal and tracks the vehicle’s longitudinal position. Since image processing algorithms are computationally intensive, this document addresses how adaptive image cropping improves the update frequency of the vision-based range sensor. A basic model of a vehicle is then derived for which a Proportional-Integral (PI) controller with gain scheduling is used for ACC. A simulation of the system determines whether the ACC algorithm will work on an actual vehicle.

Este projeto apresenta o desenvolvimento de um controle longitudinal e lateral para um veículo terrestre de categoria pesada, usando o conceito de geração de curvas de Clothoids. O controle é em malha fechada, com realimentação de velocidade e posição global (X,Y) do veículo no plano bi-dimensional. Dentro de uma arquitetura de controle autônomo para um veículo, o controle longitudinal ajusta a velocidade de cruzeiro em função da trajetória e o lateral é responsável por regular a direção do volante e a sua correspondência para com os pneus, que por sua vez direcionam o veículo dentro da trajetória dada. Para este controle, para o modelo do veículo foi apenas considerado a estrutura do cavalo mecânico (conjunto monolítico formado pela cabine, motor e rodas de tração do caminhão), desprezando qualquer carga traseira engatado nele. Primeiramente será apresentada uma breve introdução abordando a história e projetos atuas de veículos autônomos, em seguida é feito uma revisão dos conceitos básicos usados no projeto. No capitulo seguinte é abordado o modelo matemático do veículo (cinemática e dinâmica) e logo em seguida teremos a secção que aborda sobre a estrutura de controle proposta. A seguir será apresentado a seção de discussão sobre a implementação e resultados práticos. Finalmente é apresentado a conclusão e uma breve descrição sobre trabalhos futuros. / This project presents the development of a longitudinal and lateral control for a Heavy Category Ground Vehicles, using the concept of generation of curves Clothoids. This control is closed loop with feedback speed and position (X,Y) ofvehicle in two-dimensional plane. Within an autonomous control architecture for a vehicle, the longitudinal control adjusts cruising speed on the path and the lateral control is responsible for regulating direction of steering wheel and its correspondence to the tires, which in turn drive the vehicle within the given path. For this control, the vehicle model we are only considering the horse (monolithic assembly formed by the cab, engine and truck drive wheels), disregarding any rear cargo engaged in it. First a brief introduction will be presented addressing the history and projects of autonomous vehicles, then it is made a review of the basic concepts used in the project. The next chapter is discussed the mathematical model of the vehicle (kinematics and dynamics) and soon we will have a section dealing on the proposed control structure.The following will show the discussion section on the implementation and practical results, then the conclusion and a brief description of future work.

<p>An Adaptive Cruise Controller (ACC) is an extension of an ordinary cruise controller. In addition to maintaining a desired set velocity, an ACC can also maintain a desired time gap to the vehicle ahead. For this end, both the engine andthe brakes are controlled. </p><p>The purpose with this thesis has been to develop control strategies for an ACC used in heavy vehicles. The focus of the work has been the methods used for switching between the use of engine and brake. Two different methods have been studied, a hybrid controller and an MPC-controller. </p><p>For the hybrid controller, the main contribution has been to use the influence of the surroundings on the acceleration of the truck. This consists of several parts such as wind drag, road slope and rolling resistance. The estimated influence of the surroundings is used as a switch point between the use of engine and brakes. Ideally, these switch points give bumpless actuator switches. </p><p>The interest in the MPC-controller as an alternative solution was to achieve automatic actuator switching, thus with no explicitly defined switch points. The MPC-controller is based on a model of the system including bounds on the control signals. Using this knowledge, the MPC-controller will choose the correct actuator for the current driving situation. </p><p>Results from simulations show that both methods solve the actuator switch problem. The advantages with the hybrid controller are that it is implementable in a truck with the hardware used today and that it is relatively simple to parameterise. A drawback is that explicit switch points between the uses of the different actuators have to be included. The advantages with the MPC-controller are that no explicit switch points have to be introduced and that constraints and time delays on signals in the system can be handled in a simple way. Among the drawbacks, it can be mentioned that the variant of MPC, used in this thesis, is too complex to implement in the control system currently used in trucks. One further important drawback is that MPC demands a mathematical model of the system.</p>

<p>Genom att fusionera (sammanföra) data från olika sensorer kan ett resultat uppnås som ger mer än de enskilda sensorerna var för sig. Här utreds vilka sensorer och sensorfusionsmetoder som kan vara aktuella att använda för Scanias adaptiva farthållare (ACC). Den adaptiva farthållaren anpassar det egna fordonets hastighet inte bara till en förinställd hastighet utan även till framförvarande fordons hastighet. Scanias ACC-system använder idag en radar för måldetektering.</p><p>Det finns ett antal algoritmer och arkitekturer som passar för sensorfusionssystem beroende på nivån hos sensordatan och användningsområdet. Minstakvadratmetoder kan användas då data ska matchas mot en fysisk modell, ofta med lågnivådata. När tillgänglig data är på den nivån att den används direkt för att fatta beslut kan sannolikhetsmetoder användas. Intelligent fusion består av kognitiva modeller som avser härma den mänskliga beslutsprocessen. Till detta passar data på hög nivå.</p><p>Två lösningar, för två olika sensoruppsättningar, har tagits fram. Båda lösningarna är uppbyggda av bayesiska nätverk. Det första nätverket fusionerar data från den befintliga radarenheten med data från en kamera som detekterar vägmarkeringar. Resultaten visar att filbyten kan upptäckas tidigare i och med fusionen. Det andra nätverket använder sig av två radarenheter, den ursprungliga samt en likadan enhet till, vilket resulterar i ett bredare synfält. Nätverket avgör vilka mål hos respektive radar som kan anses vara samma matchade mål. Informationen kan användas för att öka redundansen i systemet samt för att upptäcka mål tidigare än förut.</p> / <p>By fusing data from different sensors a result can be achieved that is worth more than the data from each sensor by itself. Which sensors and sensor fusion methods that could be used in Scania's adaptive cruise control system (ACC) is investigated. The ACC system adapts the vehicle's speed not only to a driver decided set speed but also to the speed of preceding vehicles. Scania's ACC system uses a radar for target detection.</p><p>There exists a number of algorithms and architectures fit for use in sensor fusion systems. Which one to use depends on the level of the data to be fused and on the field of application. Least square methods are used when matching data to a physical model, data to be used is often at a low level. When working with data at decision level, probability methods are favored. Another example is intelligent fusion, cognitive methods intending to mimic the human decision process. Suitable data is data at a high level.</p><p>Two solutions, for two different sensor sets, are proposed. Both solutions are made out of Bayesian networks. The first one fuses data from the existing radar unit with data from a camera which detects lane markings. The results show that lane changes can be detected earlier thanks to the fusion. The second network uses two radar sensors of the same kind as the first, resulting in a wider field of view. The network decides which ones of each radars targets that are the same matching targets. This information could be used to increase the redundancy of the system and to detect targets earlier.</p>

An Adaptive Cruise Controller (ACC) is an extension of an ordinary cruise controller. In addition to maintaining a desired set velocity, an ACC can also maintain a desired time gap to the vehicle ahead. For this end, both the engine andthe brakes are controlled. The purpose with this thesis has been to develop control strategies for an ACC used in heavy vehicles. The focus of the work has been the methods used for switching between the use of engine and brake. Two different methods have been studied, a hybrid controller and an MPC-controller. For the hybrid controller, the main contribution has been to use the influence of the surroundings on the acceleration of the truck. This consists of several parts such as wind drag, road slope and rolling resistance. The estimated influence of the surroundings is used as a switch point between the use of engine and brakes. Ideally, these switch points give bumpless actuator switches. The interest in the MPC-controller as an alternative solution was to achieve automatic actuator switching, thus with no explicitly defined switch points. The MPC-controller is based on a model of the system including bounds on the control signals. Using this knowledge, the MPC-controller will choose the correct actuator for the current driving situation. Results from simulations show that both methods solve the actuator switch problem. The advantages with the hybrid controller are that it is implementable in a truck with the hardware used today and that it is relatively simple to parameterise. A drawback is that explicit switch points between the uses of the different actuators have to be included. The advantages with the MPC-controller are that no explicit switch points have to be introduced and that constraints and time delays on signals in the system can be handled in a simple way. Among the drawbacks, it can be mentioned that the variant of MPC, used in this thesis, is too complex to implement in the control system currently used in trucks. One further important drawback is that MPC demands a mathematical model of the system.

abstract: Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design and control objectives for ground vehicles. One central objective was to show how off-the-shelf (low-cost) remote-control (RC) “toy” vehicles can be converted into intelligent multi-capability robotic-platforms for conducting FAME research. This is shown for two vehicle classes: (1) six differential-drive (DD) RC vehicles called Thunder Tumbler (DDTT) and (2) one rear-wheel drive (RWD) RC car called Ford F-150 (1:14 scale). Each DDTT-vehicle was augmented to provide a substantive suite of capabilities as summarized below (It should be noted, however, that only one DDTT-vehicle was augmented with an inertial measurement unit (IMU) and 2.4 GHz RC capability): (1) magnetic wheel-encoders/IMU for(dead-reckoning-based) inner-loop speed-control and outer-loop position-directional-control, (2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-ultrasound-based outer-loop cruise-position-directional-separation-control, (3) Arduino motor-shield for inner-loop motor-speed-control, (4)Raspberry Pi II computer-board for demanding outer-loop vision-based cruise- position-directional-control, (5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional-control (exploiting WiFi to send video back to laptop), (6) forward-pointing ultrasonic distance/rangefinder sensor for outer-loop separation-control, and (7) 2.4 GHz spread-spectrum RC capability to replace original 27/49 MHz RC. Each “enhanced”/ augmented DDTT-vehicle costs less than 􀀀175 but offers the capability of commercially available vehicles costing over 􀀀500. Both the Arduino and Raspberry are low-cost, well-supported (software wise) and easy-to-use. For the vehicle classes considered (i.e. DD, RWD), both kinematic and dynamical (planar xy) models are examined. Suitable nonlinear/linear-models are used to develop inner/outer-loopcontrol laws. All demonstrations presented involve enhanced DDTT-vehicles; one the F-150; one a quadrotor. The following summarizes key hardware demonstrations: (1) cruise-control along line, (2) position-control along line (3) position-control along curve (4) planar (xy) Cartesian stabilization, (5) cruise-control along jagged line/curve, (6) vehicle-target spacing-control, (7) multi-robot spacing-control along line/curve, (8) tracking slowly-moving remote-controlled quadrotor, (9) avoiding obstacle while moving toward target, (10) RC F-150 followed by DDTT-vehicle. Hardware data/video is compared with, and corroborated by, model-based simulations. In short, many capabilities that are critical for reaching the longer-term FAME goal are demonstrated. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

Este projeto apresenta o desenvolvimento de um controle longitudinal e lateral para um veículo terrestre de categoria pesada, usando o conceito de geração de curvas de Clothoids. O controle é em malha fechada, com realimentação de velocidade e posição global (X,Y) do veículo no plano bi-dimensional. Dentro de uma arquitetura de controle autônomo para um veículo, o controle longitudinal ajusta a velocidade de cruzeiro em função da trajetória e o lateral é responsável por regular a direção do volante e a sua correspondência para com os pneus, que por sua vez direcionam o veículo dentro da trajetória dada. Para este controle, para o modelo do veículo foi apenas considerado a estrutura do cavalo mecânico (conjunto monolítico formado pela cabine, motor e rodas de tração do caminhão), desprezando qualquer carga traseira engatado nele. Primeiramente será apresentada uma breve introdução abordando a história e projetos atuas de veículos autônomos, em seguida é feito uma revisão dos conceitos básicos usados no projeto. No capitulo seguinte é abordado o modelo matemático do veículo (cinemática e dinâmica) e logo em seguida teremos a secção que aborda sobre a estrutura de controle proposta. A seguir será apresentado a seção de discussão sobre a implementação e resultados práticos. Finalmente é apresentado a conclusão e uma breve descrição sobre trabalhos futuros. / This project presents the development of a longitudinal and lateral control for a Heavy Category Ground Vehicles, using the concept of generation of curves Clothoids. This control is closed loop with feedback speed and position (X,Y) ofvehicle in two-dimensional plane. Within an autonomous control architecture for a vehicle, the longitudinal control adjusts cruising speed on the path and the lateral control is responsible for regulating direction of steering wheel and its correspondence to the tires, which in turn drive the vehicle within the given path. For this control, the vehicle model we are only considering the horse (monolithic assembly formed by the cab, engine and truck drive wheels), disregarding any rear cargo engaged in it. First a brief introduction will be presented addressing the history and projects of autonomous vehicles, then it is made a review of the basic concepts used in the project. The next chapter is discussed the mathematical model of the vehicle (kinematics and dynamics) and soon we will have a section dealing on the proposed control structure.The following will show the discussion section on the implementation and practical results, then the conclusion and a brief description of future work.

The thesis deals with the design, realisation and functional verification of an electronic cruise control unit. Required inputs, outputs and controller of the control unit are identified first. The design and layout of the electronical components, including the processor, power supply and other necessary hardware is discussed. The algorithm for the control unit is developed in C/C++ and thoroughly analysed. The functionality of the cruise control unit equipped with the algorithm is verified through a case study performed on a combustion engine.