Les études détaillées d'amas de galaxies confirmés à grands redshifts sont peu nombreuses. L’objectif de cette Thèse est d’établir le premier catalogue d'amas confirmés spectroscopiquement à grand redshift et, pour la première fois à ces redshifts, d’étudier de manière statistique les propriétés des galaxies membres des amas. Dans cette Thèse, nous caractérisons et étudions 20 candidats amas à redshifts 1.4 < z < 2.8 parmi les candidats les plus prometteurs de l’échantillon CARLA. Nous réduisons et analysons des données spectroscopiques grism sans fente et imagerie proche-infrarouge des amas candidats, obtenues avec le télescope spatial Hubble. Nous mesurons plus de 700 redshifts au sein des champs observés, et confirmons spectroscopiquement 16 amas CARLA dans l’intervalle 1.4 < z < 2.8; ces amas sont associés à des noyaux galactiques actifs à fortes émissions radios (RLAGN) en leur centre, par sélection. Cet effort fait plus que doubler le nombre d’amas confirmés à ces redshifts. Nous étudions également le taux de formation stellaire des galaxies membres des amas en fonction de leur masses stellaires, et de la distance aux RLAGN. Nous trouvons que les galaxies membres massives sont situées sous la séquence principale jusqu’à z=2, ce qui suggère déjà à ces redshifts une évolution accélérée des galaxies massives au sein des amas. Nous trouvons également une concentration plus importante de membres actifs à plus petits rayons des RLAGN, jusqu’à z=2. Ceci est en accord avec un renversement de la relation densité vs. taux de formation stellaire pour nos amas CARLA à 1.4 < z < 2.0, ce qui suggère que les amas CARLA représentent une phase de transition de l’évolution des galaxies au sein des amas. Nous étudions également les populations stellaires de deux de nos amas confirmés à redshift z=2.0. Nous analysons les relations couleurs-couleurs et couleurs-magnitudes de ces deux amas et montrons que l’une des structures à z=2 possède une séquence rouge de galaxies passives. Globalement, nos résultats démontrent que les amas CARLA représentent des structures riches comprenant des populations mixtes de galaxies évoluées et massives sans formation stellaire, et des galaxies actives formant des étoiles. Cet échantillon sans précédent de 16 amas confirmés spectroscopiquement dans l’intervalle de redshift 1.4 < z < 2.8 constitue un échantillon idéal pour étudier statistiquement la phase de transition des amas de galaxies, ainsi que les mécanismes de suppression de la formation stellaire. (Abrégé) / Detailed studies of high-redshift confirmed galaxy clusters are based on a few individual objects. In this Thesis, we therefore aim at building the first sample of spectroscopically confirmed clusters at high-redshifts and, for the first time at these redshifts, statistically infer cluster member galaxy properties. In this Thesis, we study and characterize 20 cluster candidates at redshifts 1.4 < z < 2.8, which represent the most promising cluster candidates from the CARLA sample. We reduce and analyze slitless grism spectroscopic and near-infrared imaging data of the fields, obtained with the Hubble Space Telescope. We measure redshifts for over 700 star-forming sources in the 20 fields, and we spectroscopically confirm 16 CARLA clusters in the range 1.4 < z < 2.8; by selection, these clusters are associated with powerful radio-loud active galactic nuclei (RLAGN) at their center. This effort alone more than doubles the number of confirmed clusters at these redshifts. We study cluster member star-formation rates (SFRs) as a function of their stellar masses and distances from the RLAGN. We find that massive members are located below their star-forming main-sequence up to z=2. This implies that the massive star-forming end of the cluster population already followed an accelerated evolution at these high redshifts. We also find an increasing concentration of star-forming members with smaller radii relative to the RLAGN, at all redshifts up to z=2. Our 1.4 < z < 2.0 cluster members are therefore consistent with a reversal of the SFR-density relation. This is a first evidence showing that CARLA clusters represent a transition phase for cluster galaxy evolution. We also study stellar populations of two of our confirmed CARLA clusters at z=2.0. We study their color-color and color-magnitude relations and show that one of the two structures is comprised of a z=2 red sequence of passive candidate members. Together, these results provide clear evidence that our confirmed CARLA clusters represent rich structures comprised of mixed populations, including both evolved, passive, massive galaxies, and galaxies with ongoing star formation. Together, this unprecedented sample of 16 confirmed clusters at 1.4 < z < 2.8 constitutes an ideal sample for further statistical investigation of the cluster transition phase, including study of quenching mechanisms. (Abridged)

The subject of self-driving car technology has drawn growing interest in recent years. Many companies, such as Baidu and Tesla, have already introduced automatic driving techniques in their newest cars when driving in a specific area. However, there are still many challenges ahead toward fully autonomous driving cars. Tesla has caused several severe accidents when using autonomous driving functions, which makes the public doubt self-driving car technology. Therefore, it is necessary to use the simulator environment to help verify and perfect algorithms for the perception, planning, and decision-making of autonomous vehicles before implementation in real-world cars. This project aims to build a benchmark for implementing the whole self-driving car system in software. There are three main components including perception, planning, and control in the entire autonomous driving system. This thesis focuses on two sub-tasks 2D object detection and semantic segmentation in the perception part. All of the experiments will be tested in a simulator environment called The CAR Learning to Act(Carla), which is an open-source platform for autonomous car research. Carla simulator is developed based on the game engine(Unreal4). It has a server-client system, which provides a flexible python API. 2D object detection uses the You only look once(Yolov4) algorithm that contains the tricks of the latest deep learning techniques from the aspect of network structure and data augmentation to strengthen the network’s ability to learn the object. Yolov4 achieves higher accuracy and short inference time when comparing with the other popular object detection algorithms. Semantic segmentation uses Efficient networks for Computer Vision(ESPnetv2). It is a light-weight and power-efficient network, which achieves the same performance as other semantic segmentation algorithms by using fewer network parameters and FLOPS. In this project, Yolov4 and ESPnetv2 are implemented into the Carla simulator. Two modules work together to help the autonomous car understand the world. The minimal distance awareness application is implemented into the Carla simulator to detect the distance to the ahead vehicles. This application can be used as a basic function to avoid the collision. Experiments are tested by using a single Nvidia GPU(RTX2060) in Ubuntu 18.0 system. / Ämnet självkörande bilteknik har väckt intresse de senaste åren. Många företag, som Baidu och Tesla, har redan infört automatiska körtekniker i sina nyaste bilar när de kör i ett specifikt område. Det finns dock fortfarande många utmaningar inför fullt autonoma bilar. Detta projekt syftar till att bygga ett riktmärke för att implementera hela det självkörande bilsystemet i programvara. Det finns tre huvudkomponenter inklusive uppfattning, planering och kontroll i hela det autonoma körsystemet. Denna avhandling fokuserar på två underuppgifter 2D-objekt detektering och semantisk segmentering i uppfattningsdelen. Alla experiment kommer att testas i en simulatormiljö som heter The CAR Learning to Act (Carla), som är en öppen källkodsplattform  för autonom bilforskning. Du ser bara en gång (Yolov4) och effektiva nätverk för datorvision (ESPnetv2) implementeras i detta projekt för att uppnå Funktioner för objektdetektering och semantisk segmentering. Den minimala distans medvetenhets applikationen implementeras i Carla-simulatorn för att upptäcka avståndet till de främre bilarna. Denna applikation kan användas som en grundläggande funktion för att undvika kollisionen.

Perceiving the environment is a crucial aspect of autonomous vehicles. To plan the route, the autonomous vehicle needs to be able to detect objects such as cars and pedestrians. This is possible through 3D object detection. However, labeling this type of data is time-consuming. By utilizing a virtual environment, there is an opportunity to generate data and label it in a quicker manner. This thesis aims to investigate how well three selected state-of-the-art models perform on a synthetic dataset of point cloud data. The results showed that the models attain a higher average precision compared to a dataset from the real world. This is mainly due to the virtual environment’s simplicity in relation to the real world’s detail. The results also suggest that models using different representations of point cloud data have different capabilities of transferring knowledge to the real world. / Att uppfatta miljön är en avgörande aspekt av autonoma fordon. Till planera rutten behöver det autonoma fordonet kunna upptäcka föremål som bilar och fotgängare. Detta är möjligt genom 3D-objektdetektering. Att märka denna typ av data är dock tidskrävande. Genom att använda en virtuell miljö, finns det en möjlighet att generera data och märka dem på ett snabbare sätt sätt. Denna avhandling syftar till att undersöka hur väl tre valda state-of-the-art modeller utför på en syntetiskt dataset av punktmolndata. Resultaten visade att modellerna uppnår en average precision jämfört med ett dataset från den riktiga världen. Detta beror främst på den virtuella miljöns enkelhet i förhållande till den verkliga världens detaljer. Resultaten tyder också på att modeller som använder olika representationer av punktmolnsdata har olika möjligheter att överföra kunskap till den verkliga världen.

O objetivo do presente trabalho é o de realizar uma análise do cinema brasileiro contemporâneo, em específico o período que ficou conhecido como o cinema da Retomada. Procurei analisar o cinema da Retomada a partir de duas perspectivas teóricas, que levam em consideração tanto a análise interna dos filmes, quanto de uma análise dos aspectos que dizem respeito à forma como ele se organiza, buscando compreender o contexto social e político em que ele se insere, quais são as principais figuras que se destacam dentro do cenário cinematográfico que vai da segunda metade dos anos 1990 até o início dos anos 2000. Em relação ao primeiro aspecto, tomaram-se como marcos os filmes Carlota Joaquina, princesa do Brazil (1995), dirigido por Carla Camurati, e Cidade de Deus (2002), dirigido por Fernando Meirelles e codirigido por Kátia Lund. Serão esses os filmes que analiso neste trabalho, procurando identificar os grupos sociais retratados pelas películas, o sistema de relações estabelecidos entre eles, além de considerar as recepções da crítica e as trajetórias sociais de seus respectivos diretores. E em relação ao segundo aspecto, foi possível identificar no contexto da Retomada, a presença de características que definem um grupo de cineastas em torno de um sentido sociológico de geração, cujas figuras somente se inserem na área cinematográfica devido a uma conjuntura específica, decorrente das transformações institucionais e políticas do Estado brasileiro nesse fim do século XX. / The objective of the present study is to carry out an analysis of contemporary Brazilian cinema, specifically the period that became known as the Cinema of Retomada. I tried to analyze the cinema of Retomada from two theoretical perspectives, which take into account both the internal analysis of the films and an analysis of the aspects that relate to the way in which it is organized, trying to understand the social and political context, who are the main figures that stand out in the cinematographic scenario, that goes from the second half of the 1990s to the beginning of the 2000s. In relation to the first aspect, the films Carlota Joaquina, princess of Brazil (1995), directed by Carla Camurati, and City of God (2002), directed by Fernando Meirelles and co-directed by Kátia Lund. These are the films that I analyze in this work, trying to identify the social groups portrayed by the films, the system of relations established among them, besides considering the receptions of criticism and the social trajectories of their respective directors. In relation to the second aspect, it was possible to identify in the context of Retomada, the presence of characteristics that define a group of filmmakers around a sociological sense of generation, whose figures are only inserted in the cinematographic area due to a specific conjuncture, due to the transformations of the Brazilian State at the end of the twentieth century.

O objetivo do presente trabalho é o de realizar uma análise do cinema brasileiro contemporâneo, em específico o período que ficou conhecido como o cinema da Retomada. Procurei analisar o cinema da Retomada a partir de duas perspectivas teóricas, que levam em consideração tanto a análise interna dos filmes, quanto de uma análise dos aspectos que dizem respeito à forma como ele se organiza, buscando compreender o contexto social e político em que ele se insere, quais são as principais figuras que se destacam dentro do cenário cinematográfico que vai da segunda metade dos anos 1990 até o início dos anos 2000. Em relação ao primeiro aspecto, tomaram-se como marcos os filmes Carlota Joaquina, princesa do Brazil (1995), dirigido por Carla Camurati, e Cidade de Deus (2002), dirigido por Fernando Meirelles e codirigido por Kátia Lund. Serão esses os filmes que analiso neste trabalho, procurando identificar os grupos sociais retratados pelas películas, o sistema de relações estabelecidos entre eles, além de considerar as recepções da crítica e as trajetórias sociais de seus respectivos diretores. E em relação ao segundo aspecto, foi possível identificar no contexto da Retomada, a presença de características que definem um grupo de cineastas em torno de um sentido sociológico de geração, cujas figuras somente se inserem na área cinematográfica devido a uma conjuntura específica, decorrente das transformações institucionais e políticas do Estado brasileiro nesse fim do século XX. / The objective of the present study is to carry out an analysis of contemporary Brazilian cinema, specifically the period that became known as the Cinema of Retomada. I tried to analyze the cinema of Retomada from two theoretical perspectives, which take into account both the internal analysis of the films and an analysis of the aspects that relate to the way in which it is organized, trying to understand the social and political context, who are the main figures that stand out in the cinematographic scenario, that goes from the second half of the 1990s to the beginning of the 2000s. In relation to the first aspect, the films Carlota Joaquina, princess of Brazil (1995), directed by Carla Camurati, and City of God (2002), directed by Fernando Meirelles and co-directed by Kátia Lund. These are the films that I analyze in this work, trying to identify the social groups portrayed by the films, the system of relations established among them, besides considering the receptions of criticism and the social trajectories of their respective directors. In relation to the second aspect, it was possible to identify in the context of Retomada, the presence of characteristics that define a group of filmmakers around a sociological sense of generation, whose figures are only inserted in the cinematographic area due to a specific conjuncture, due to the transformations of the Brazilian State at the end of the twentieth century.

The transportation industry has been transforming because of rapid digitalization and autonomy. Because of this the demand for more connected and autonomous vehicles is increasing for both private individuals and businesses. Reducing human interaction emphasizes the need for higher road safety. Autonomous vehicles, in general, have different sources of faults which might lead to severe accidents and injuries. Testing and validating autonomous vehicles can be useful for avoiding such cases. Remote driving is a potential fallback option whenever autonomous vehicles fail. The remote operator can take direct or indirect control of the remotely-operated vehicle whenever the need arises. Tele-operated driving has three main parts - the vehicle, the remote operator, and communication between the two. Communication plays an important role in this feedback control system. Any communication disturbance in the video feed from the vehicle to the remote operator or in the commands from the operator to the vehicle can result in safety violations and even accidents. These disturbances can have different sources. This work presents a methodology to inject network disturbances to analyze the effect of these disturbances on vehicle manoeuvrability. A driving simulator, CARLA, was used as a vehicle model to solve this problem and to allow human-in-the-loop. NETEM was used to inject different faults on the outgoing traffic to emulate network disturbances. The implementation was done on LocalHost to avoid any delays that might occur due to the presence of physical devices in the network. It was concluded from the Time-to-Collision (TTC) results that road safety decreased whenever the fault was injected in a vehicle-following case. Another important insight was that the packet loss of 5% always showed a TTC violation for a 6-sec threshold. The highest steering reversal rate was also observed for 5% packet loss. It was observed from the results that the steering reversal rate (SRR) was consistently higher in the faulty run. This indicates that the drivers were more distracted. Based on the results, it is observed that network disturbances affected driving in a remote driving setup. The results can be further utilized for more comprehensive studies to understand how simulator-in-loop can be used for testing, verification, and validation. / Transportbranschen har förändrats på grund av snabb digitalisering och autonomi. Efterfrågan på mer uppkopplade och autonoma fordon ökar hos både privatpersoner och företag. Men minskad användarinteraktion ökar behovet av högre säkerhet hos fordonen. Autonoma fordon har i allmänhet olika felkällor som kan leda till allvarliga olyckor och skador. Att testa och validera autonoma fordon blir viktigt för att undvika sådana fall. Fjärrkörning är ett potentiellt komplement när autonoma fordon inte är tillräckligt säkra. Fjärroperatören kan ta direkt eller indirekt kontroll över det fjärrmanövrerade fordonet när behovet uppstår. Telestyrd körning har tre huvudkomponenter - fordonet, fjärroperatören och kommunikationen däremellan. Kommunikation spelar en viktig roll i detta återkopplade system. Varje störning i kommunikationen av videoflödet från fordonet till fjärroperatören eller i kommandon från operatören till fordonet kan resultera i bristande säkerhet och till och med olyckor. Dessa störningar kan ha olika källor. Detta arbete presenterar en metod för att injicera nätverksstörningar för att kunna analysera effekten av dessa på fordonets manövrerbarhet. En körsimulator, CARLA, användes som fordonsmodell och anpassades för att kunna styras av en mänsklig fjärroperatör. NETEM användes för att injicera olika fel på den utgående nätverkstrafiken för att efterlikna nätverksstörningar. Implementeringen gjordes på LocalHost för att undvika fördröjningar som kan uppstå på grund av närvaron av andra fysiska enheter i nätverket. Av TTC-resultaten drogs slutsatsen att trafiksäkerheten minskade när fel injicerades i ett fall där fjärroperatören följer att annat fordon. En annan viktig insikt var att en paketförlust på 5% alltid gav överträdelser med för låg TTC vid en gräns för lägsta tillåtna värde på 6 sekunder. Även de högsta observerade värdena på styrvändningstakt (steering reversal rate) observerades för 5% paketförlust. Resultaten visade att styrvändningstakten konsekvent var högre vid felinjicering. Detta tyder på att förarna var mer distraherade. Baserat på resultaten är en observeration att nätverksstörningar kan påverka säkerheten vid fjärroperation. Metodiken kan användas för mer omfattande studier för att förstå hur simulator-i-loopen kan användas för testning, verifiering och validering.

For safe mobility, an autonomous vehicle must perceive the surroundings accurately. There are many perception tasks associated with understanding the local environment such as object detection, localization, and lane analysis. Object detection, in particular, plays a vital role in determining an object’s location and classifying it correctly and is one of the challenging tasks in the self-driving research area. Before employing an object detection module in autonomous vehicle testing, an organization needs to have a precise analysis of the module. Hence, it becomes crucial for a company to have an evaluation framework to evaluate an object detection algorithm’s performance. This thesis develops a comprehensive framework for evaluating and analyzing object detection algorithms, both 2D (camera images based) and 3D (LiDAR point cloud-based). The pipeline developed in this thesis provides the ability to evaluate multiple models with ease, signified by the key performance metrics, Average Precision, F-score, and Mean Average Precision. 40-point interpolation method is used to calculate the Average Precision. / För säker rörlighet måste ett autonomt fordon uppfatta omgivningen exakt. Det finns många uppfattningsuppgifter associerade med att förstå den lokala miljön, såsom objektdetektering, lokalisering och filanalys. I synnerhet objektdetektering spelar en viktig roll för att bestämma ett objekts plats och klassificera det korrekt och är en av de utmanande uppgifterna inom det självdrivande forskningsområdet. Innan en anställd detekteringsmodul används i autonoma fordonsprovningar måste en organisation ha en exakt analys av modulen. Därför blir det avgörande för ett företag att ha en utvärderingsram för att utvärdera en objektdetekteringsalgoritms prestanda. Denna avhandling utvecklar ett omfattande ramverk för utvärdering och analys av objektdetekteringsalgoritmer, både 2 D (kamerabilder baserade) och 3 D (LiDAR-punktmolnbaserade). Rörledningen som utvecklats i denna avhandling ger möjlighet att enkelt utvärdera flera modeller, betecknad med nyckelprestandamätvärdena, Genomsnittlig precision, F-poäng och genomsnittlig genomsnittlig precision. 40-punkts interpoleringsmetod används för att beräkna medelprecisionen.

3D ranging sensor LiDAR, is an extensively used sensor in the autonomous vehicle industry, but LiDAR placement problem is not studied extensively. This thesis work proposes a framework in an open- source autonomous driving simulator (CARLA) that aims to solve LiDAR placement problem, based on the tasks that LiDAR is intended for in most of the autonomous vehicles. LiDAR placement problem is solved by improving point cloud density around the vehicle, and this is calculated by using LiDAR Occupancy Boards (LOB). Introducing LiDAR Occupancy as an objective function, the genetic algorithm is used to optimize this problem. This method can be extended for multiple LiDAR placement problem. Additionally, for multiple LiDAR placement problem, LiDAR scan registration algorithm (NDT) can also be used to find a better match for first or reference LiDAR. Multiple experiments are carried out in simulation with a different vehicle truck and car, different LiDAR sensors Velodyne 16 and 32 channel LiDAR, and, by varying Region Of Interest (ROI), for testing the scalability and technical robustness of the framework. Finally, this framework is validated by comparing the current and proposed LiDAR positions on the truck. / 3D- sensor LiDAR, är en sensor som används i stor utsträckning inom den autonoma fordonsindustrin, men LiDAR- placeringsproblemet studeras inte i stor utsträckning. Detta uppsatsarbete föreslår en ram i en öppen källkod för autonom körningssimulator (CARLA) som syftar till att lösa LiDAR- placeringsproblem, baserat på de uppgifter som LiDAR är avsedda för i de flesta av de autonoma fordonen. LiDAR- placeringsproblem löses genom att förbättra punktmolntätheten runt fordonet, och detta beräknas med LiDAR Occupancy Boards (LOB). Genom att introducera LiDAR Occupancy som en objektiv funktion används den genetiska algoritmen för att optimera detta problem. Denna metod kan utökas för flera LiDAR- placeringsproblem. Dessutom kan LiDAR- scanningsalgoritm (NDT) för flera LiDAR- placeringsproblem också användas för att hitta en bättre matchning för LiDAR för första eller referens. Flera experiment utförs i simulering med ett annat fordon lastbil och bil, olika LiDAR-sensorer Velodyne 16 och 32kanals LiDAR, och, genom att variera intresseområde (ROI), för att testa skalbarhet och teknisk robusthet i ramverket. Slutligen valideras detta ramverk genom att jämföra de nuvarande och föreslagna LiDAR- positionerna på lastbilen.

Evaluating the challenges and opportunities of cooperative autonomous vehicles (CAV) require an adapted simulation methodology reproducing realistic driving and sensory contexts. In this paper, we propose a RounD-like CARLA scenario reproducing in CARLA the driving context recorded in the RounD dataset. We focus in particular on roundabout scenarios, as they are considered particularly challenging for CAV. We present the methodology followed to generate the CARLA scenario and describe challenges to reproduce trajectories corresponding to RounD. Origin and destination of vehicles, waypoint and speed are extracted from RounD for CARLA vehicles to closely reproduce the driving patterns observed in RounD. The benefit of such scenario are manyfold, such as evaluating control algorithms of CAVs, deep AI reinforcement learning, or vehicular sensor data sampling under realistic driving contexts. It notably will reduce the gap of AI mechanisms for CAV between simulation scenarios and realistic conditions.

Despite the tremendous development in the autonomous vehicle industry, the tools for systematic testing are still lacking. Real-world testing is time-consuming and above all, dangerous. There is also a lack of a framework to automatically generate critical scenarios to test autonomous vehicles. This thesis develops a general framework for end- to- end testing of an autonomous vehicle in a simulated environment. The framework provides the capability to generate and execute a large number of traffic scenarios in a reliable manner. Two methods are proposed to compute the criticality of a traffic scenario. A so-called critical value is used to learn the probability distribution of the critical scenario iteratively. The obtained probability distribution can be used to sample critical scenarios for testing and for benchmarking a different autonomous vehicle. To describe the static and dynamic participants of urban traffic scenario executed by the simulator, OpenDrive and OpenScenario standards are used. / Trots den enorma utvecklingen inom den autonoma fordonsindustrin saknas fortfarande verktygen för systematisk testning. Verklig testning är tidskrävande och framför allt farlig. Det saknas också ett ramverk för att automatiskt generera kritiska scenarier för att testa autonoma fordon. Denna avhandling utvecklar en allmän ram för end-to-end- test av ett autonomt fordon i en simulerad miljö. Ramverket ger möjlighet att generera och utföra ett stort antal trafikscenarier på ett tillförlitligt sätt. Två metoder föreslås för att beräkna kritiken i ett trafikscenario. Ett så kallat kritiskt värde används för att lära sig sannolikhetsfördelningen för det kritiska scenariot iterativt. Den erhållna sannolikhetsfördelningen kan användas för att prova kritiska scenarier för testning och för benchmarking av ett annat autonomt fordon. För att beskriva de statiska och dynamiska deltagarna i stadstrafikscenariot som körs av simulatorn används OpenDrive och OpenScenario-standarder.