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.

<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.

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With all the advancements in autonomous and connected cars, there is a developing body of research around the security and robustness of driving automation systems. Attacks and mitigations for said attacks have been explored, but almost always solely in software simulations. For this thesis, I led a team to build the foundation for an open source platoon of scale semi-autonomous vehicles. This work will enable future research into implementing theoretical attacks and mitigations. Our 1/10 scale car leverages an Nvidia Jetson, embedded microcontroller, and sensors. The Jetson manages the computer vision, networking, control logic, and overall system control; the embedded microcontroller directly controls the car. A lidar module is responsible for recording distance to the preceding car, and an inertial measurement unit records the velocity of the car itself. I wrote the software for the networking, interprocess, and serial communications, as well as the control logic and system control.

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Traffic signals are used at intersections to manage the flow of vehicles by allocating right-of-way in a timely manner for different users of the intersection. Traffic signals are therefore installed at an intersection to improve overall safety and to decrease vehicular average delay. However, the variation of driving speed in response to these signals causes an increase in fuel consumption and air emission levels. One solution to this problem is Eco-Cooperative Adaptive Cruise Control (Eco-CACC), which attempts to reduce vehicle fuel consumption and emission levels by optimizing driver behavior in the vicinity of a signalized intersection. Various Eco-CACC algorithms have been proposed by researchers to address this issue. With the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, algorithms are being developed that utilize signal phasing and timing (SPaT) data together with queue information to optimize vehicle trajectories in the vicinity of signalized intersections. The research presented in this thesis constitutes the third phase of a project that entailed developing and evaluating an Eco-CACC system. Its main objective is to evaluate the benefits of the newly developed Eco-CACC algorithm that was proposed by the Center for Sustainable Mobility at the Virginia Tech Transportation Institute. This algorithm uses advanced signal information (SPaT) to compute the fuel-optimal trajectory of vehicles, and, then, send recommended speeds to drivers as an audio message or implement them directly into the subject vehicle. The objective of this study is to quantitatively quantify the fuel-efficiency of the Eco-CACC system in a real field environment. In addition, another goal of this study is to address the implementation issues and challenges with the field application of the Eco-CACC system. A dataset of 2112 trips were collected as part of this research effort using a 2014 Cadillac SRX equipped with a vehicle onboard unit for (V2V) and (V2I) communication. A total of 32 participants between the ages of 18 and 30 were randomly selected from one age group (18-30) with an equal number of males and females. The controlled experiment was conducted on the Virginia Smart Road facility during daylight hours for dry pavement conditions. The controlled field experiment included four different scenarios: normal driving, driving with red indication countdown information provided to drivers, driving with recommended speed information computed by the Eco-CACC system and delivered to drivers, and finally automated driving (automated Eco-CACC system). The controlled field experiment was conducted for four values of red indication offsets along an uphill and downhill approach. The collected data were compared with regard to fuel economy and travel time over a fixed distance upstream and downstream of the intersection (820 ft (250 m) upstream of the intersection to 590 ft (180 m) downstream for a total length of 1410 ft (430 m)). The results demonstrate that the Eco-CACC system is very efficient in reducing fuel consumption levels especially when driving downhill. The field data indicates that the automated scenario could produce fuel and travel time savings of 31% and 9% on average, respectively. In addition, the study demonstrates that driving with a red indication countdown and recommended speed information can produce fuel savings ranging from 4 to 21 percent with decreases in travel times ranging between 1 and 10 percent depending on the value of red indication offset and the direction. Split-split-plot design was used to analyze the data and test significant differences between the four scenarios with regards to fuel consumption and travel time. The analysis shows that the differences between normal driving and driving with either the manual or automated Eco-CACC systems are statistically significant for all the red indication offset values. / Master of Science