<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>

<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>

<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.

The automotive industry is going through a massive transformation sparked by integrating emergingtechnologies within cars, such as automation and digital connectivity. These technologies were among theenablers of in-vehicle digital services. These services are deployed in the in-vehicle infotainment (IVI)system. While current IVI systems prioritize driver safety, they often overlook the needs of passengers forimmersive entertainment experiences. This oversight becomes especially critical as OEM companiestransition to level 4 autonomous cars, where driver distraction is no longer problematic. Therefore, thisthesis explores how automotive companies in Sweden will adapt their infotainment services when shifting tolevel 4 autonomous cars. An interpretive qualitative study is conducted with Polestar employees. Two mainthemes emerge 1) Current state of Digital Services in IVI Systems and 2) Future Directions of DigitalServices in IVI Systems within Level 4 Autonomous Vehicles. Findings highlight challenges faced by appdevelopers, adoption criteria for new technology, and strategies for future-proofing IVI digital services.Implications are discussed, and future directions of IVI digital services are proposed in the discussionsection. Finally, the manuscript concludes with suggestions for limitations and future research, inviting theaudience to contribute to the ongoing discourse.

Syftet med denna studie var att undersöka det framtida användandet av instrumentpanelen i autonoma personbilar. En lösning presenteras där interiören liknar en tågkupé med säten vända mot varandra kring den nya instrumentpanelen utformad som ett multifunktionellt bord. Genom att undersöka vad konsumenter från fyra olika målgrupper ville sysselsätta sig med i en nivå fem autonom personbil kunde funktioner såsom bildskärmar, tangentbord och förvaringsmöjligheter inkluderas i den nya instrumentpanelen.   För insamling av empiri användes både kvalitativa och kvantitativa metoder där semistrukturerade intervjuer och en enkätundersökning genomfördes. Båda metoderna riktade sig till fyra målgrupper av konsumenter: Studenter, barnfamiljer, kortvägspendlare samt resande säljare. För att samla in mycket information på kort tid utfördes metoderna samtidigt och båda metoderna användes för att validera resultatet.   Genom analysen av empirin hittades gemensamma intressen mellan målgrupperna, i båda metoderna, och sammanställde dessa till kundönskemål. Resultatet av analysen visar bland annat att passagerare i autonoma fordon vill ha bra möjligheter till att arbeta, lyssna på musik, docka telefon, laptop eller surfplatta till inbyggda skärmar i bilen samt läsa och skriva email. Det önskas hållare för drycker, avlastningsytor för mat samt kyld förvaring.   Intervjuer med experter från branschen genomfördes för att bistå med utformning- och säkerhetskrav som tillsammans med kundönskemålen gav ett underlag för att generera koncept. Innan konceptegenereringsfasen påbörjades gjordes en brainstorming för att diskutera tekniska lösningar till de framtagna önskemålen. De framtagna koncepten utvärderades med metoden för Pughs konceptvalsmatris där de mättes mot ett referenskoncept. Ett vinnande koncept kunde efter förbättringar utses och presenteras med skisser, produktbeskrivning samt en produktspecifikation.  Arbetet begränsades till att fokusera på att uppfylla kundönskemålen och lämnar många krav runt säkerhet åt framtida vidareutveckling av konceptet. / Contents of this bachelor’s thesis are written in Swedish.  The purpose of this study was to investigate the future use of the instrument panel in autonomous cars. A solution is presented in which the interior resembles a train compartment with seats facing each other around the new instrument panel designed as a multifunctional table. By examining what consumers from four different target groups would want to engage themselves with in a level five autonomous car, features such as monitors, keyboards and storage facilities was included in the new instrument panel.  For the gathering of empirical data, qualitative and quantitative methods was used, where both semi-structured interviews and a survey was conducted. Both methods addressed four target groups of consumers: students, families with children, short-distance commuters and traveling salespersons. To collect much information in a short period of time, the methods were performed simultaneously and both methods were used to validate the result.  The empirical analysis found common interests between the target groups, in both methods and compiled these into customer requests. The result of the analysis shows, among other things, that passengers in autonomous cars want good opportunities to work, listen to music, dock their phone, laptop or tablet too built-in monitors in the car as well as read and write email. They desired holder for drinks, relief surfaces when eating food as well as refrigerated storage.  Interviews with industry experts were conducted to complement with design and safety requirements that, together with customer requests, provided a basis for generating concepts. Before the start of the concept generating phase, a brainstorming was conducted to discuss technical solutions to the desired customer requests. The final concepts were evaluated using the method of Pugh Concept Selection, where they were compared against a reference concept. A winning concept was, after improvements, presented with sketches, product description and a product specification.  The work was limited to focusing on meeting customer requests and leaving many requirements for personal safety to future, further development of the concept.

Rapid progress is being made towards the realization of autonomous cars. Since the technology is in its early stages, human intervention is still necessary in order to ensure hazard-free operation of autonomous driving systems. Substantial research efforts are underway to enhance driver and passenger safety in autonomous cars. Toward that end GreedyHaarSpiker, a real-time vision-based lane detection algorithm is proposed for road lane detection in different weather conditions. The algorithm has been implemented in Python 2.7 with OpenCV 3.0 and tested on a Raspberry Pi 3 Model B ARMv8 1GB RAM coupled to a Raspberry Pi camera board v2. To test the algorithm’s performance, the Raspberry Pi and the camera board were mounted inside a Jeep Wrangler. The algorithm performed better in sunny weather with no snow on the road. The algorithm’s performance deteriorated at night time or when the road surface was covered with snow.

The widespread adoption of autonomous cars has the potential to revolutionize urban transportation, but what impact will it have on urban form? This thesis examines the hypothesis that adopting autonomous cars can transform street space into a more human-centric purpose, leading to more livable and sustainable cities. The research was conducted through a literature review, analysis of case studies, and the development of specific street designs in order to reveal possible scenarios. The literature review suggests that adopting autonomous cars can reduce the need for parking and increase the efficiency of transportation. Furthermore, the rise of shared cars is expected to revolutionize the way people move. With the advent of autonomous cars, it is possible that personal cars will become less necessary as people can rely on these constant-moving vehicles for transportation. These changes will impact our cities creating new opportunities to improve the urban space. The thesis explores these challenges and opportunities through design for the actual urban environment of Washington D.C. As the capital of the United States, the country where cars have significantly shaped its cities, it is also home to influential political and policy-makers. As a result, the city offers a good opportunity to rethink the future urban environment when this technology will be widely adopted. The findings of this thesis suggest that the adoption of autonomous cars has the potential to transform urban form reclaiming street space for people, but also requires careful planning and design to ensure that the benefits are distributed equitably and the negative impacts are minimized. The thesis concludes with four street proposals, each performing a different role in the city and the results provoke a reflection of the role of the street in our cities. / Master of Science / The widespread use of self-driving cars can transform our lives in cities. This new technology could lead to a more human-centered urban environment, where streets are designed for people rather than cars. The use of self-driving cars could also reduce the need for parking and improve the efficiency of transportation. However, this transformation requires careful planning and design to ensure that the benefits are distributed fairly and that negative impacts are minimized. A recent study looked at the potential impact of self-driving cars in Washington D.C., and suggests that the adoption of this technology could transform urban form and make cities more livable and sustainable. The study concludes with several street design proposals that could help shape the future of our cities. The findings of this thesis suggest that the adoption of autonomous cars has the potential to transform urban form reclaiming street space for people, it concludes with street proposals, each performing a different role in the city.

L’autopartage est une manière moderne de louer une voiture. C'est un système attractif pour les clients qui utilisent une voiture occasionnellement. Dans un système d’autopartage, une flotte de véhicules est répartie sur une aire urbaine. Les client peuvent prendre ou rendre une voiture à n'importe quel moment et à n'importe quelle station, à condition qu’il y ait une voiture de libre à la station de départ et qu’il y a une place de parking libre à la station de destination. Pour s'en assurer, les clients peuvent réserver une voiture en avance. Pour qu’un tel système fonctionne de manière satisfaisante, il faut que le nombre de véhicules et le nombre de places libres dans les stations s'équilibrent. Cela conduit à un problème d'équilibre d'occupation des stations, appelé problème de relocalisation : un opérateur doit surveiller l'occupation des stations et décider quand et de quelle manière les voiture doivent être deplacées d’une station « trop pleine » à une station « insuffisamment pleine ». Nous considérons un système d’autopartage innovant, où les voitures sont partiellement autonomes. Cela permet de constituer des convois de véhicules que dirige un véhicule spécial, de sorte qu'un convoi est mis en mouvement par un seul conducteur. Cette configuration est similaire au système mis en place pour les vélos en libre-service, où un camion peut déplacer plusieurs vélos simultanément pendant le processus de la relocalisation. Dans le cadre de cette thèse, nous développons les aspects dynamiques et statiques du problème de relocalisation. Le « problème de relocalisation dynamique » décrit la situation où les voitures sont déplacées pendant les heures de travail afin de satisfaire les besoins des clients. L’opérateur doit prendre des décisions « dynamiques », en fonction de la situation. Dans le cadre du « problème de relocalisation statique », nous supposons qu’il n'y a aucune interaction (ou très peu) entre les clients et le système. Cette situation se produit lorsque le système est préparé pour le lendemain (ex : processus de la relocalisation effectué pendant la nuit). Nous modélisons le problème de relocalisation dans le cadre d’un système de tâches métriques. Ensuite, nous analysons les deux problèmes et nous donnons des stratégies pour les résoudre. Enfin, nous effectuons quelques expériences de calcul pour examiner l’applicabilité des algorithmes présentés en pratique. / Carsharing is a modern way of car rental, attractive to customers who make only occasional use of a car on demand. In a carsharing system, a fleet of cars is distributed at specified stations in an urban area, customers can take a car at any time and station and return it at any time and station, provided that there is a car available at the start station and a free place at the destination station. To ensure the latter, customers have to book their demands in advance. For operating such a system in a satisfactory way, the stations have to keep a good ratio between the total number of places and the number of cars in each station, in order to serve as many requests as possible. This leads to the problem of balancing the load of the stations, called Relocation Problem: an operator has to monitor the load and to decide when and how to move cars from “overfull” stations to “underfull” ones. We consider an innovative carsharing system, where the cars are partly autonomous, which allows to build wireless convoys of cars leaded by a special vehicle, such that the whole convoy is moved by only one driver. This setting is similar to bikesharing, where trucks can simultaneously move several bikes during the relocation process. In this thesis, we address the dynamic and static aspects of the Relocation Problem. The “Dynamic Relocation Problem” describes the situation when cars can be moved between stations during the working hours in order to satisfy the needs of the customers. Hereby, the operator has to make decisions dynamically according to the current situation. In the “Static Relocation Problem” we assume that there is no (or only little) interaction by customers with the system. This situation occurs when the carsharing system is prepared for the next day, i.e., the relocation process is performed during the night. We model the Relocation Problem in the framework of a metric task system. Afterwards, we theoretically analyze both problems and give strategies to solve them. Finally, we perform some computational experiments to examine the applicability of the presented algorithms in practice.

In the context of designing and implementing ethical Artificial Intelligence (AI), varying perspectives exist regarding developing trustworthy AI for autonomous cars. This study sheds light on the differences in perspectives and provides recommendations to minimize such divergences. By exploring the diverse viewpoints, we identify key factors contributing to the differences and propose strategies to bridge the gaps. This study goes beyond the trolley problem to visualize the complex challenges of trustworthy and ethical AI. Three pillars of trustworthy AI have been defined: transparency, reliability, and safety. This research contributes to the field of trustworthy AI for autonomous cars, providing practical recommendations to enhance the development of AI systems that prioritize both technological advancement and ethical principles.