With the rapid advancement of autonomous vehicles, reliable and efficient wireless communication systems with high data rates have become essential for their safe and efficient operation and further evolution. High data rates are found in the higher frequency bands where conductive antennas lack radiation efficiency. To achieve high radiation efficiency, researchers tend towards using Circular Polarized Dielectric Resonator Antennas (CP-DRA). However, there is a lack of studies that cover the FR2 5G bands n257, n258, n261 suggested by 3GPP which is needed if vehicles were to drive across regional borders. This project addresses the challenges of achieving suitable CP-DRA performance for autonomous vehicle communication aimed at covering these FR2 5G bands. The objective is to design and simulate an optimized CP-DRA antenna that meets the required performance characteristics for further use in a phased array for efficient communication in the high-frequency FR2 5G bands. The objective was fulfilled by producing a model of a CP-DRA antenna that covers the mentioned FR2 5G bands. The antenna array achieves this with an axial ratio beam width at plus/minus 20 degrees azimuth angle and peak gain of 9-12 dBi throughout the frequency range. The model consists of four cylindrical resonator antenna elements excited in phase quadrature by a slot aperture feeding network to accomplish the circular polarization. The radiation efficiency of the model is 94% throughout the frequency range with an impedance bandwidth of < -15 dB. A prototype was built and tested that vaguely verified the beam pattern and center frequency. Future work includes building a prototype more comparable to the model for further verification of the circularly polarized gain pattern.

This thesis is to study the optimisation problems in autonomous vehicles, especially the modelling and optimisation of collision avoidance, and to develop some optimisation algorithms based on genetic algorithms and neural networks to operate autonomous vehicles without any collision. Autonomous vehicles, also called self-driving vehicles or driverless vehicles are completely robotised driving frameworks to allow the vehicle to react to outside conditions within a bunch of calculations to play out the undertakings. This thesis summarised artificial intelligence and optimisation techniques for autonomous driving systems in the literature. The optimisation problems related to autonomous vehicles are categorised into four groups: lane change, motion planner, collision avoidance, and artificial intelligence. A chart had been developed to summarise those research and related optimisation methods to help future researchers in the selection of optimisation methods Collision Avoidance is one of streamlining issues in autonomous vehicles. Several sensors had been used to identify position and dangers and collision avoidance algorithms had been developed to analyse the dangers and to use vehicles to avoid a collision. In this thesis, the current research on collision avoidance has been reviewed and some challenges and future works were presented to select the research direction of this thesis, the aim of this research will be the development of optimisation methods to avoid collisions in a predefined environment. The contributions of this thesis are that (1) a simulation model had been developed using Matlab for collision avoidance and serval scenarios were proposed and experimented with. The sensors are used as the inputs to determine collision in the learning preparation of the algorithm; (2) a neural network was used for collision avoidance of autonomous vehicles; (3) a new method was proposed with the combination of genetic algorithm and neural network. In the proposed frame, the neural network is used for decision making and a genetic algorithm is used for the training of the neural network. The results and experimentation show that the proposed strategies are well in the designed environment.

The mining industry uses vehicles for a wide range of applications, including excavation and transportation of rock and soil. Currently, this requires a lot of human labour, mainly drivers, but efforts are being made to increase automation, e.g. using autonomous vehicles. In order for a vehicle to reach any level of autonomy, it needs to be aware of its surroundings, for instance by using sensors. The placement of the sensors is a difficult problem. The goal of this project was to create a tool that would simplify the sensor placement process. The tool should simulate sensors on autonomous vehicles, both by visualizing their field of view and by generating synthetic data. The tool was created, including simulation environments, models of different types of sensors and tools to analyze the results of the simulation. Both the field of view visualization and the data analysis tools were shown to be powerful tools for evaluating sensor placements. All of the sensor models are able to generate data, with different levels of realism. The radar model and the camera model give a good estimation of what the sensors can detect, while the lidar model is capable of generating realistic data. / Gruvindustrin använder fordon till ett stort antal olika uppgifter, bland annat till att gräva ut och förflytta sten och jord. Detta kräver för tillfället mycket manuellt arbete, framförallt förare, men försök att automatisera delar av arbetet utförs, till exempel genom att använda autonoma fordon. För att ett fordon ska kunna bli autonomt krävs det att det kan känna av sin omgivning, exempelvis genom att använda sensorer. Sensorplacering är ett svårt problem. Målet med projektet var att skapa ett verktyg för att förenkla sensorplaceringsprocessen. Verktyget skulle simulera sensorer på autonoma fordon, både genom att visualisera deras synfält och genom att generera syntetisk data. Verktyget skapades, inklusive simuleringsmiljöer, modeller av olika typer av sensorer, och verktyg för att analysera genererad data. Både synfältsvisualiseringen och datagenereringen visades vara användbara verktyg för att utvärdera sensorplaceringar. Alla sensormodellerna kan generera data, med olika realistiska resultat. Radarmodellen och kameramodellen ger en bra uppskattning av vad sensorerna kan detektera, medan lidarmodellen kan generera realistisk data.

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In searching for more efficient vehicles with lower carbon emissions, researchers have invested enormous time and resources in designing new materials, components, systems, and control methods. The result is not only an immense volume of publications and patents but also a true electrification revolution in the transportation sector. Although the advancements are remarkable, much is still to be developed. Energy management systems are often designed to fulfil drive cycles that represent just a fraction of the actual use of the vehicles, disregarding essential factors such as driving conditions that may vary in real life. Furthermore, control algorithms should not ignore one of the most relevant driving aspects, comfort. Driving should be a pleasant activity since we spend much time of our lives performing this task. This research proposes a novel algorithm for managing energy consumption in electrified vehicles, the regen-based equivalent consumption minimization strategy (R-ECMS). Its suitability for solving the power-split problem is evaluated. Experiments emulating labelling schedules are conducted considering an example application. Robustness to different drive cycles and flexibility of the algorithm to various modes of operation are assessed. Furthermore, the method is integrated into an autonomous longitudinal control. The function leverages vehicle dynamics and journey mapping to assure energy efficiency and adequate drivability. Finally, the tests are conducted using human-driven cycles leveraging driving simulation technology. That allows for including driver subjective feelings in the design and assessing the algorithm's performance in realistic driving conditions. / Thesis / Doctor of Philosophy (PhD)

Human error has been the most common cause of car accidents. Advances in sensing and data fusion have made recent progress in autonomous vehicles that will increase the potential of drastically improving safety, efficiency, and cost of transportation. In this thesis, we present an overview of finding the error probability of sensor fusion in automotive driving, and we will investigate the collision probabilities in automated vehicles. In our study, we simulate automated driving systems in a virtual environment using real-world maps using MATLAB Automated Driving Toolbox, Simulink, and Roadrunner. During the study, we will investigate different scenarios such as weather conditions, noise, lighting, and road conditions with an ‘ego- vehicle’ equipped with multiple sensors such as; lidar and vision sensors.

Optimizing routes for multiple autonomous vehiclesin complex traffic situations can lead to improved efficiency intraffic. Attempting to solve these optimization problems centrally,i.e. for all vehicles involved, often lead to algorithms that exhibitthe curse of dimensionality: that is, the computation time andmemory needed scale exponentially with the number of vehiclesresulting in infeasible calculations for moderate number ofvehicles. However, using a numerical framework called tensordecomposition one can calculate and store solutions for theseproblems in a more manageable way. In this project, we investi-gate different tensor decomposition methods and correspondingalgorithms for solving optimal control problems, by evaluatingtheir accuracy for a known solution. We also formulate complextraffic situations as optimal control problems and solve them.We do this by using the best tensor decomposition and carefullyadjusting different cost parameters. From these results it canbe concluded that the Sequential Alternating Least Squaresalgorithm used with canonical tensor decomposition performedthe best. By asserting a smooth cost function one can solve certainscenarios and acquire satisfactory solutions, but it requiresextensive testing to achieve such results, since numerical errorsoften can occur as a result of an ill-formed problem. / Att optimera färdvägen för flertalet au-tonoma fordon i komplexa trafiksituationer kan leda till effekti-vare trafik. Om man försöker lösa dessa optimeringsproblemcentralt, för alla fordon samtidigt, leder det ofta till algorit-mer som uppvisar The curse of dimensionality, vilket är då beräkningstiden och minnes-användandet växer exponentielltmed antalet fordon. Detta gör många problem olösbara för endasten måttlig mängd fordon. Däremot kan sådana problem hanterasgenom numeriska verktyg så som tensornedbrytning. I det här projektet undersöker vi olika metoder för tensornedbrytningoch motsvarandes algoritmer för att lösa optimala styrproblem,genom att jämföra dessa för ett problem med en känd lösning.Dessutom formulerar vi komplexa trafiksituationer som optimalastyrproblem för att sedan lösa dem. Detta gör vi genom attanvända den bästa tensornedbrytningen och genom att noggrantanpassa kostnadsparametrar. Från dessa resultat framgår det att Sequential Alternating Least Squaresalgoritmen, tillsammans medkanonisk tensornedbrytning, överträffade de andra algoritmersom testades. De komplexa trafiksituationerna kan lösas genomatt ansätta släta kostnadsfunktioner, men det kräver omfattandetestning för att uppnå sådana resultat då numeriska fel lätt kan uppstå som ett resultat av dålig problemformulering. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm