Global ETD Search

81	ASEMS: Autonomous Specific Energy Management Strategy Amirfarhangi Bonab, Saeed January 2019 (has links) This thesis addresses the problem of energy management of a hybrid electric power unit for an autonomous vehicle. We introduce, evaluate, and discuss the idea of autonomous-specific energy management strategy. This method is an optimization-based strategy which improves the powertrain fuel economy by exploiting motion planning data. First, to build a firm base for further evaluations, we will develop a high-fidelity system-level model for our case study using MATLAB/Simulink. This model mostly concerns about energy-related aspects of the powertrain and the vehicle. We will derive and implement the equations for each of the model subsystems. We derive model parameters using available data in the literature or online. Evaluation of the developed model shows acceptable conformity with the actual dynamometer data. We will use this model to replace the built-in rule-based logic with the proposed strategy and assess the performance.\par Second, since we are considering an optimization-based approach, we will develop a novel convex representation of the vehicle and powertrain model. This translates to reformulating the model equations using convex functions. Consequently, we will express the fuel-efficient energy management problem as the convex optimization problem. We will solve the optimization problem using dedicated numerical solvers. Extracting the control inputs using this approach and applying them on the high-fidelity model provides similar results to dynamic programming in terms of fuel consumption but in substantially less amount of time. This will act as a pivot for the subsequent real-time analysis.\par Third, we will perform a proof-of-concept for the autonomous-specific energy management strategy. We implement an optimization-based path and trajectory planning for a vehicle in the simplified driving scenario of a racing track. Accordingly, we use motion planning data to obtain the energy management strategy by solving an optimization problem. We will let the vehicle to travel around the circuit with the ability to perceive and plan up to an observable horizon using the receding horizon approach. Developed approach for energy management strategy shows a substantial reduction in the fuel consumption of the high-fidelity model, compared to the rule-based controller. / Thesis / Master of Science in Mechanical Engineering (MSME) / The automotive industry is on the verge of groundbreaking transformations as a result of electrification and autonomous driving. Electrified autonomous car of the future is sustainable, energy-efficient, more convenient, and safer. In addition to the advantages of electrification and autonomous driving individually, the intersection and interaction of these mainstreams provide new opportunities for further improvements on the vehicles. Autonomous cars generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. This thesis considers the case of an autonomous hybrid electric vehicle and presents the novel idea of autonomous-specific energy management strategy. Specifically, this thesis is a proof-of-concept, a trial to exploit the motion planning data for a self-driving car to improve the fuel economy of the hybrid electric power unit by adopting a more efficient energy management strategy. With the ever-increasing number of autonomous hybrid electric vehicles, particularly in the self-driving fleets, the presented method shows an extremely promising potential to reduce the fuel consumption of these vehicles. Autonomous Vehicle Hybrid Electric Vehicle Autonomous Driving Energy Management Strategy Self-driving car Optimization Convex Optimization Electrification Sustainability Optimal Control Model Predictive Control Receding Horizon Fuel Economy Path Planning Trajectory Planning Powertrain
82	Advances in Vehicle Automation: Ethics and Technology Sütfeld, Leon René 14 September 2021 (has links) With the arrival of automated vehicles (AVs) on our streets virtually around the corner, this thesis explores advances in automated driving technology with a focus on ethical decision making in dilemmatic traf- fic situations. In a total of five publications, we take a multi-facetted approach to analyse and address the core challenges related to auto- mated ethical decision making in AVs. In publications one through three, we conduct a series of immersive virtual reality studies to analyze human behavior in traffic dilemmas, explore mathematical approaches to model the decision making process, investigate how the assessment methodology can affect moral judgment, and discuss the implications of these studies for algorithmic decision making in the real-world. In publication number four, we provide a comprehensive summary of the status quo of AV technology and legislation with regard to automated ethical decision making. Here, we discuss when and why ethical deci- sion making systems become necessary in AVs, review existing guide- lines for the behavior of AVs in dilemma situations, and compile a set of 10 demands and open questions that need to be addressed in the pursuit of a framework for ethical decision making in AVs. Finally, the basis for automated ethical decision making in AVs will be provided by accurate assessments of the immediate environment of the car. The pri- mary technology used to provide the required information processing of camera and LiDAR images in AVs is machine learning, and in particular deep learning. In publication five, we propose a form of adaptive acti- vation functions, addressing a central element of deep neural networks, which could, for instance, lead to increased detection rates of relevant objects, and thus help to provide a more accurate assessment of the AVs environment. Overall, this thesis provides a structured and compre- hensive overview of the state of the art in ethical decision making for AVs. It includes important implications for the design of decision mak- ing algorithms in practice, and concisely outlines the central remaining challenges on the road to a safe, fair and successful introduction of fully automated vehicles into the market. automated vehicles ethical decision making moral decision making behavioral ethics trolley dilemma self-driving cars decision making systems AI ethics vehicular automation traffic dilemmas deep learning adaptive blending units activation functions dilemma situations virtual reality autonomous vehicles J.4 - SOCIAL AND BEHAVIORAL SCIENCES ddc:150
83	Moraliska bedömningar av autonoma systems beslut / Moral judgments of autonomous intelligent systems Lindelöf, Gabriel Trim Olof January 2020 (has links) Samhällsutvecklingen går i en riktning där människor arbetar i allt närmare samarbete med artificiella agenter. För att detta samarbete ska vara på användarens villkor är det viktigt att förstå hur människor uppfattar och förhåller sig till dessa system. Hur dessa agenter bedöms moraliskt är en komponent i denna förståelse. Malle m.fl. (2015) utförde en av de första studierna kring hur normer och skuld appliceras på människa respektive robot. I samma artikel efterfrågades mer forskning kring vilka faktorer hos agenter som påverkar de moraliska bedömningarna. Föreliggande studie tog avstamp i denna frågeställning och avsåg att undersöka hur moralisk godtagbarhet och skuldbeläggning skiljde sig beroende på om agenten var en person, en humanoid robot eller ett autonomt intelligent system utan kropp (AIS). Ett mellangrupps-experiment (N = 119) användes för att undersöka hur agenterna bedömdes för sina beslut i tre olika moraliska dilemman. Deltagares rättfärdigaden bakom bedömningar samt medveten hållning utforskades som förklaringsmodell av skillnader. Medveten hållning avser Dennetts (1971) teori kring huruvida en agent förstås utifrån mentala egenskaper. Resultaten visade att person och robot erhöll liknande godtagbarhet för sina beslut medan AIS fick signifikant lägre snitt. Graden skuld som tillskrevs skiljde sig inte signifikant mellan agenterna. Analysen av deltagares rättfärdiganden gav indikationer på att skuldbedömningarna av de artificiella agenterna inte grundade sig i sådan information som antagits ligga till grund för denna typ av bedömningar. Flera rättfärdiganden påpekade också att det var någon annan än de artificiella agenterna som bar skulden för besluten. Vidare analyser indikerade på att deltagare höll medveten hållning mot person i störst utsträckning följt av robot och sedan AIS. Studien väcker frågor kring huruvida skuld som fenomen går att applicera på artificiella agenter och i vilken utsträckning distribuerad skuld är en faktor när artificiella agenter bedöms. Artificial intelligence HRI Moral judgments Blame Permissibility Intentional stance Humanoid robot Autonomous intelligent systems Self-driving cars Moral dilemmas Moral psychology Moral machine Artificiell intelligens Människa-robotinteraktion Moraliska bedömningar Moraliska dilemman Skuld Klander Distribuerad skuld Humanoid robot Medveten hållning Självkörande bilar Moralpsykologi Human Computer Interaction
84	Neurala nätverk försjälvkörande fordon : Utforskande av olika tillvägagångssätt / Neural Networks for Autonomous Vehicles : An Exploration of Different Approaches Hellner, Simon, Syvertsson, Henrik January 2021 (has links) Artificiella neurala nätverk (ANN) har ett brett tillämpningsområde och blir allt relevantare på flera håll, inte minst för självkörande fordon. För att träna nätverken användsmeta-algoritmer. Nätverken kan styra fordonen med hjälp av olika typer av indata. I detta projekt har vi undersökt två meta-algoritmer: genetisk algoritm (GA) och gradient descent tillsammans med bakåtpropagering (GD & BP). Vi har även undersökt två typer av indata: avståndssensorer och linjedetektering. Vi redogör för teorin bakom de metoder vi har försökt implementera. Vi lyckades inte använda GD & BP för att träna nätverk att köra fordon, men vi redogör för hur vi försökte. I resultatdelen redovisar vi hur det med GA gick att träna ANN som använder avståndssensorer och linjedetektering som indata. Sammanfattningsvis lyckades vi implementera självkörande fordon med två olika typer av indata. / Artificial Neural Networks (ANN) have a broad area of application and are growing increasingly relevant, not least in the field of autonomous vehicles. Meta algorithms are used to train networks, which can control a vehicle using several kinds of input data. In this project we have looked at two meta algorithms: genetic algorithm (GA), and gradient descent with backpropagation (GD & BP). We have looked at two types of input to the ANN: distance sensors and line detection. We explain the theory behind the methods we have tried to implement. We did not succeed in using GD & BP to train ANNs to control vehicles, but we describe our attemps. We did however succeeded in using GA to train ANNs using a combination of distance sensors and line detection as input. In summary we managed to train ANNs to control vehicles using two methods of input, and we encountered interesting problems along the way. artificial neural networks gradient descent genetic algorithm backpropagation unity self driving autonomous vehicles line detection neural network neural net artificiella neurala nätverk neurala nätverk självkörande bilar självkörande fordon unity bakåtpropagering linjedetektering gradient descent genetisk algoritm neurala nät Computer Sciences Datavetenskap (datalogi)
85	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik
86	Dynamic Speed Adaptation for Curves using Machine Learning / Dynamisk hastighetsanpassning för kurvor med maskininlärning Narmack, Kirilll January 2018 (has links) The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated. / Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades. Machine Learning Artificial Intelligence MachineLearning Machine-Learning AI ML MLP Neural Networks Multilayer-Perceptron RFB RBF-Network Vehicle Automation AutonomousVehicles Autonomous SelfDriving Self-Driving Self Driving Curve Speed Adaptation Adaption CSA ACC Adaptive Cruise Control Driving Style Driver Behavior Behaviour Sample Samples Distance Derivative Speed Velocity Curvature Road Inclination Lane Width Type Acceleration Longitudinal Lateral Data Training Test Validation Results Discussion Sustainability Ethics Ethical Sustainable Future Today Tomorrow Yesterday Robot Robotics System Systems Class Classes Bin Bins Tree One A Dynamic Using Time Delay Volvo Car Cars Corporation Zenuity School Computer Science Master Degree Project Thesis Paper Object GPS Map Length Research Advanced Машинное Обучение Искусственный Интеллект AI ML MLP RBF Автомобиль Машына Сеть Робот Водитель Заворот Дорога Сам Сама Едет Ехать Учить Учит Учится Автоматизация Адаптация Результат Один Одна Одно Вчера Сегодня Завтра Дерево Карта ГПС Информатика Компьютер Наука Научная Работа Школа Вольво Завод Транспорт Maskininlärning Artificiell Intelligens Inlärning Maskin AI ML MLP RBF Neural Neurala Nätverk Artificiella Automation Själv Självkörande Körande Bil Fordon Robot Robotik Körstil Stil Beteende Adaption Kurva Lutning Väg CSA ACC Farthållare Hastighet Fart Hållare Inclination Körfält Fält Spår Bredd Typ Acceleration Longitudinell Lateral Data Traingin Test Validation Resultat Diskussion Längd Hållbarhet Etik Etiskt Framtid Dåtid Idag Imorgon System Class Klass Classer Klasser Träd En Ett Fler Flera Dynamisk Använda Tid Fördröjning Tids Volvo Car Corporation Object Zenuity Skola Dator Vetenskap Forkning Avancerad Projekt Exjobb Examensarbete Betyg GPS Karta Computer Sciences Datavetenskap (datalogi)

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