<div>Self driving cars becoming more popular nowadays, which transport with it's own intelligence and take appropriate actions at adequate time. Safety is the key factor in driving environment. A simple fail of action can cause many fatalities. Computer Vision has major part in achieving this, it help the autonomous vehicle to perceive the surroundings. Detection is a very popular technique in helping to capture the surrounding for an autonomous car. At the same time tracking also has important role in this by providing dynamic of detected objects. Autonomous cars combine a variety of sensors such as RADAR, LiDAR, sonar, GPS, odometry and inertial measurement units to perceive their surroundings. Driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collision Mitigation by Breaking (CMbB) ensure safety while driving.</div><div>Perceiving the information from environment include setting up sensors on the car. These sensors will collect the data it sees and this will be further processed for taking actions. The sensor system can be a single sensor or multiple sensor. Different sensors have different strengths and weaknesses which makes the combination of them important for technologies like Autonomous Driving. Each sensor will have a limit of accuracy on it's readings, so multi sensor system can help to overcome this defects. This thesis is an attempt to develop a multi sensor multi object tracking method to perceive the surrounding of the ego vehicle. When the Object detection gives information about the presence of objects in a frame, Object Tracking goes beyond simple observation to more useful action of monitoring objects. The experimental results conducted on KITTI dataset indicate that our proposed state estimation system for Multi Object Tracking works well in various challenging environments.</div>

This degree project investigates the suitability of using Behavior Trees (BT) as an architecture for the behavioral layer in autonomous driving. BTs originate from video game development but have received attention in robotics research the past couple of years. This project also includes implementation of a simulated traffic environment using the Unity3D engine, where the use of BTs is evaluated and compared to an implementation using finite-state machines (FSM). After the initial implementation, the simulation along with the control architectures were extended with additional behaviors in four steps. The different versions were evaluated using software maintainability metrics (Cyclomatic complexity and Maintainability index) in order to extrapolate and reason about more complex implementations as would be required in a real autonomous vehicle. It is concluded that as the AI requirements scale and grow more complex, the BTs likely become substantially more maintainable than FSMs and hence may prove a viable alternative for autonomous driving.

Bilar av ett visst märke, modell, år och uppsättning funktioner börjar med ett pris som fastställs av tillverkaren. När de åldras och säljs vidare som de används, är de föremål för prissättning av utbud och efterfrågan för deras speciella uppsättning funktioner, utöver deras unika historia. Ju mer detta skiljer dem från jämförbara bilar, desto svårare blir de att utvärdera med traditionella metoder. Genom att använda maskininlärning algoritmer för att bättre utnyttja data om alla mindre vanliga egenskaper hos en bil kan man mer exakt bedöma ett fordons värde. Denna studie jämför prestandan för algoritmer för Linjär Regression, Ridge Regression, Lasso Regression och Random Forest Regression när det gäller att förutsäga priset på begagnade bilar. En viktig kvalifikation för ett prisförutsägelseverktyg är att avskrivningar kan representeras för att bättre utnyttja tidigare data för aktuell prisförutsägelse. Denna studie jämför därför även den skattade prisavtagningen hos algoritmerna. Studien har genomförts med en stor offentlig datauppsättning av begagnade bilar. Resultaten visar att Random Forest Regression visar den högsta prisförutsägelseprestanda för alla mätvärden som används. Den kunde också representera den genomsnittliga avskrivningen mycket närmare verkligheten än de andra algoritmerna, med 13,7 % förutspådd årlig geometrisk prisavtagning för datasetet oberoende av fordonets ålder. / Cars of a particular make, model, year, and set of features start out with a price set by the manufacturer. As they age and are resold as used, they are subject to supply-and-demand pricing for their particular set of features, in addition to their unique history. The more this sets them apart from comparable cars, the harder they become to evaluate with traditional methods. Using Machine Learning algorithms to better utilize data on all the less common features of a car can more accurately assess the value of a vehicle. This study compares the performance of Linear Regression, Ridge Regression, Lasso Regression, and Random Forest Regression ML algorithms in predicting the price of used cars. An important qualification of a price prediction tool is that depreciation can be represented to better utilize past data for current price prediction. The study has been conducted with a large public dataset of used cars. The results show that Random Forest Regression demonstrates the highest price prediction performance across all metrics used. It was also able to represent average depreciation much more closely than the other algorithms, at 13.7% predicted annual geometric depreciation for the dataset independent of vehicle age.

[pt] Esta pesquisa tem como proposta refletir sobre alguns dos significados dos automóveis construídos pelo discurso publicitário. O ponto de partida é entender que o consumo é um fato social inerente às relações socioculturais nas sociedades moderno-contemporâneas e que a publicidade, como sua principal narrativa, tem por função operar a valorização dos bens através do imaginário produzido em seus anúncios. Para a execução da pesquisa, foram retirados anúncios impressos de um jornal e de uma revista de grande circulação nacional, com o intuito de interpretar, no caso do automóvel, algumas das múltiplas significações do consumo e compreender o potencial publicitário como fornecedor de sentido aos bens. / [en] This research proposal is to think over the symbolic meanings of automobiles, based on their advertising speech. The initial conception is to understand that consumerism is a social fact inherent to socio-cultural relations in the modern-contemporary society and that advertisement, as its principal idea, is functional to operate a value in the products through imaginary speech in their ads. This research was based on print ads of newspapers and magazines of national distribution in order to infer that, in the automobile industry, advertise gives multiple consumption meanings, and to understand the advertising potential as a supplier of significance to material products.

<div><div><div> How can we transform an agricultural vehicle into an autonomous weeding robot? A robot that can run autonomously through a vegetable field, classify multiple types of weeds from real-time video feed and then spray specific herbicides based of previously classified weeds. In this research, we answer some of the theoretical and practical challenges regarding the transformation of an agricultural vehicle into an autonomous weeding robot. How can we transform an agricultural vehicle into an autonomous weeding robot? A robot that can run autonomously through a vegetable field, classify multiple types of weeds from real-time video feed and then spray specific herbicides based of previously classified weeds. In this research, we answer some of the theoretical and practical challenges regarding the transformation of an agricultural vehicle into an autonomous weeding robot. How can we transform an agricultural vehicle into an autonomous weeding robot? A robot that can run autonomously through a vegetable field, classify multiple types of weeds from real-time video feed and then spray specific herbicides based of previously classified weeds. In this research, we answer some of the theoretical and practical challenges regarding the transformation of an agricultural vehicle into an autonomous weeding robot. <br></div></div></div><div><br></div><div> First, we propose a solution for real-time crop row detection from autonomous navigation of agricultural vehicle using domain knowledge and unsupervised machine learning based approach. We implement projective transformation to transform camera image plane to an image plane exactly at the top of the crop rows, so that parallel crop rows remain parallel. Then we use color based segmentation to differentiate crop and weed pixels from background. We implement hierarchical density-based spatial clustering of applications with noise (HDBSCAN) clustering algorithm to differentiate between the crop row clusters and weed clusters. <br></div><div><br></div><div> Finally we use Random sample consensus (RANSAC) for robust line fitting through the detected crop row clusters. We test our algorithm against four different well established methods for crop row detection in-terms of processing time and accuracy. Our proposed method, Clustering Algorithm based RObust LIne Fitting (CAROLIF), shows significantly better accuracy compared to three other methods with average intersect over union (IoU) value of 73%. We also test our algorithm on a video taken from an agricultural vehicle at a corn field in Indiana. CAROLIF shows promising results under lighting variation, vibration and unusual crop-weed growth. <br></div><div><br></div><div><div> Then we propose a robust weed classification system based on convolutional neural network (CNN) and novel decision-level evidence-based multi-sensor fusion algorithm. We create a small dataset of three different weeds (Giant ragweed, Pigweed and Cocklebur) commonly available in corn fields. We train three different CNN architectures on our dataset. Based on classification accuracy and inference time, we choose VGG16 with transfer learning architecture for real-time weed classification.</div><div> </div><div> To create a robust and stable weed classification pipeline, a multi-sensor fusion algorithm based on Dempster-Shafer (DS) evidence theory with a novel entropy function is proposed. The proposed novel entropy function is inspired from Shannon and Deng entropy but it shows better results at understanding uncertainties in certain scenarios, compared to Shannon and Deng entropy, under DS framework. Our proposed algorithm has two advantages compared to other sensor fusion algorithms. First, it can be applied to both space and time domain to fuse results from multiple sensors and create more robust results. Secondly, it can detect which sensor is faulty in the sensors array and compensate for the faulty sensor by giving it lower weight at real-time. Our proposed algorithm calculates the evidence distance from each sensor and determines if one sensor agrees or disagrees with another. Then it rewards the sensors which agrees with another according to their information quality which is calculated using our novel entropy function. The proposed algorithm can combine highly conflicting evidences from multiple sensors and overcomes the limitation of original DS combination rule. After testing our algorithm with real and simulation data, it shows better convergence rate, anti-disturbing ability and transition property compared to other methods available from open literature.</div></div><div><br></div><div><div> Finally, we present a fuzzy-logic based approach to measure the confidence</div><div> of the detected object's bounding-box (BB) position from a CNN detector. The CNN detector gives us the position of BB with percentage accuracy of the object inside the BB on each image plane. But how do we know for sure that the position of the BB is correct? When we are detecting an object using multiple cameras, the position of the BB on the camera image plane may appear in different places based on the detection accuracy and the position of the cameras. But in 3D space, the object is at the exact same position for both cameras. We use this relation between the camera image planes to create a fuzzy-fusion system which will calculate the confidence value of detection. Based on the fuzzy-rules and accuracy of BB position, this system gives us confidence values at three different stages (`Low', `OK' and `High'). This proposed system is successful at giving correct confidence score for scenarios where objects are correctly detected, objects are partially detected and objects are incorrectly detected. </div></div>

Företaget Volvo Cars återfinns på Sustainable Brand Index lista över de mest hållbaravarumärkena enligt svenska konsumenter. Samtidigt verkar företaget inom en av de mestklimatbelastande branscherna. Mot bakgrund av detta och med utgångspunkten att reklam ären av de faktorerna som kan ha påverkat konsumenternas uppfattningar var syftet med dennastudie att undersöka hur hållbarhet, främst utifrån ett miljöperspektiv, kommuniceras i VolvoCars reklamfilmer. Studien har genomförts med hjälp av den kvalitativa metoden multimodalkritisk diskursanalys och utgått från ett teoretiskt ramverk bestående av teorier om CSR,grönmålning och budskapsstrategier. Materialet i studien består av tre stycken reklamfilmeroch är hämtade från Volvo Cars youtubekanal, reklamfilmerna är publicerade mellan åren2020 och 2021. Resultatet visar sammanfattningsvis att hållbarhet kommuniceras genom detre diskursiva temana hållbarhet genom miljö, hållbarhet som säkerhet och hållbarhet somnågonting modernt och att detta görs med hjälp av transformerande budskapsstrategier somdelvis kompletteras med intygsreklam. Vidare visar studien att Volvo Cars använder desemiotiska resurserna för att förmedla och transformera känslan och idén av hållbarhet,snarare än på ett sätt där det tydligt framgår vad som gör dem hållbara.

The electric car market is growing at a furious pace and cars are becoming increasingly advanced. When the internal combustion engine is replaced with an electric motor, a vacuum is created in the front of the car. Car manufacturers have taken advantage of this and created a luggage compartment in the front. The space is called frunk based on a combination of the words front and trunk.  Car owners currently have difficulty finding areas of use for the frunk as the limited geometry of the space makes it generally difficult to find objects that fit. Products with a view to creating added value for the funk are almost non-existent on the market. The purpose of the degree project is to provide use cases and concept work for a purposeful but rarely used frunk, as an attractive product range for a future Volvo car fleet, regardless of the car segment. shall be malleable according to Volvo's various car segments.  Together with Volvo Cars, the project group has developed a number of concepts that puts the user at the center. The project group has maximized the volume, modified the material and found new solutions, all to make full use of the space. To further optimize the space, the project team has created the concept frunk-kit. Frunk-kit as a concept is a business idea that includes selectable accessories gathered in a custom-made bag. The options vary according to the customer benefit for different target groups and global markets. This concept gives the customer the opportunity to choose the options that suit their lifestyle and the enhances the user experience. The Frunk-kit concept is based on finding the right use for the space and in order to be able to demonstrate the function of the frunk-kit, the project group has created a prototype where the active lifestyle is at the center based on the XC40 recharge target group. By combining the range of Volvo accessories with the frunk, we create new opportunities for the customer to get where they want, with everything they need.

<p>Advancement in modern technology has brought with it an advent of increased interest in self-driving. The rapid growth in interest has caused a surge in the development of autonomous vehicles which in turn brought with itself a few challenges. To overcome these new challenges, automotive companies are forced to invest heavily in the research and development of autonomous vehicles. To overcome this challenge, simulations are a great tool in any arsenal that’s inclined towards making progress towards a self-driving autonomous future. There is a massive growth in the amount of computing power in today’s world and with the help of the same computing power, simulations will help test and simulate scenarios to have real time results. However, the challenge does not end here, there is a much bigger hurdle caused by the growing complexities of modelling a complete simulation environment. This thesis focuses on providing a solution for modelling a RADAR sensor for a simulation environment. This research presents a RADAR modeling technique suitable for autonomous vehicle simulation environment using open-source utilities. This study proposes to customize an onboard LiDAR model to the specification of a desired RADAR field of view, resolution, and range and then utilizes a density-based clustering algorithm to generate the RADAR output on an open-source graphical engine such as Unreal Engine (UE). High fidelity RADAR models have recently been developed for proprietary simulation platforms such as MATLAB under its automated driving toolbox. However, open-source RADAR models for open-source simulation platform such as UE are not available. This research focuses on developing a RADAR model on UE using blueprint visual scripting for off-road vehicles. The model discussed in the thesis uses 3D pointcloud data generated from the simulation environment and then clipping the data according to the FOV of the RADAR specification, it clusters the points generated from an object using DBSCAN. The model gives the distance and azimuth to the object from the RADAR sensor in 2D. This model offers the developers a base to build upon and help them develop and test autonomous control algorithms requiring RADAR sensor data. Preliminary simulation results show promise for the proposed RADAR model. </p>

Autonomous cars have become a trending topic as cars become better and better at driving autonomously. One of the big changes that have allowed autonomous cars to progress is the improvements in machine learning. Machine learning has made autonomous cars able to detect and react to obstacles on the road in real time. Like in all machine learning, there exists no solution that works better than all others, each solution has different strengths and weaknesses. That is why this study has tried to find the strengths and weaknesses of the object detector You Only Look Once v8 (YOLOv8) in autonomous cars. YOLOv8 was tested for how fast and accurately it could detect pedestrians in traffic in normal daylight images and light-augmented images. The trained YOLOv8 model was able to learn to detect pedestrians at high accuracy on daylight images, with the model achieving a mean Average Precision 50 (mAP50) of 0.874 with a Frames per second (FPS) of 67. Finally, the model struggled especially when the images got darker which means that the YOLOv8 in the current stage might not be good as the main detector for autonomous cars, as the detector loses accuracy at night. More tests with other datasets are needed to find all strengths and weaknesses of YOLOv8. / Autonoma bilar har blivit ett trendigt ämne då bilar blir bättre och bättre på att köra självständigt. En av de stora förändringarna som har gjort det möjligt för autonoma bilar att utvecklas är framstegen inom maskininlärning. Maskininlärning har gjort att autonoma bilar kan upptäcka och reagera på hinder på vägen i realtid. Som i all maskininlärning finns det ingen lösning som fungerar bättre än alla andra, varje lösning har olika styrkor och svagheter. Det är därför den här studien har försökt hitta styrkorna och svagheterna hos objektdetektorn You Only Look Once v8 (YOLOv8) i autonoma bilar. YOLOv8 testades för hur snabbt och precist den kunde upptäcka fotgängare i bilder av trafiken i dagsljus och bilder där ljuset har förändrat. Den tränade YOLOv8-modellen kunde lära sig att upptäcka fotgängare med hög noggrannhet på bilder i dagsljus, där modellen uppnådde en genomsnittlig medelprecision 50 (mAP50) på 0,874 med en antal bilder per sekund (FPS) på 67. Modellen hade särskilt svårt när bilderna blev mörkare vilket gör att YOLOv8 i det aktuella stadiet kanske inte är tillräckligt bra som huvuddetektor för autonoma bilar, eftersom detektorn tappar noggrannhet på mörkare bilder. Fler tester med andra datauppsättningar behövs för att hitta alla styrkor och svagheter med YOLOv8.

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