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For the past couple of years researchers around theworld have tried to develop fully autonomous vehicles. One of theproblems that they have to solve is how to navigate in a dynamicworld with ever-changing variables. This project was initiated tolook into one scenario of the path planning problem; overtakinga human driven vehicle. Model Predictive Control (MPC) hashistorically been used in systems with slower dynamics but withadvancements in computation it can now be used in systems withfaster dynamics. In this project autonomous vehicles controlledby MPC were simulated in Python based on the kinematic bicyclemodel. Constraints were posed on the overtaking vehicle suchthat the two vehicles would not collide. Results show that anovertake, that keeps a proper distance to the other vehicle andfollows common traffic laws, is possible in certain scenarios. / Under de senaste åren har forskare världen över försökt utveckla fullt autonoma fordon. Ett av problemen som behöver lösas är hur man navigerar i en dynamisk värld med ständigt förändrande variabler. Detta projekt startades för att titta närmare på en aspekt av att planera en rutt; att köra om ett mänskligt styrt fordon. Model Predictive Control (MPC) har historiskt sett blivit använt i system med långsammare dynamik, men med framsteg inom datorers beräkningskraft kan det nu användas i system med snabbare dynamik. I detta projekt simulerades självkörande fordon, styrda av MPC, i Python. Fordonsmodellen som används var kinematic bicycle model. Begränsningar sattes på det omkörande fordonet så att de två fordonen inte kolliderar. Resultaten visar att en omkörning, som håller avstånd till det andra fordonet samt följer trafikregler, är möjligt i vissa scenarion. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

The emergence of unmanned aerial vehicles (UAVs) along with the implementation of 5G networks offers exciting opportunities in expanding wireless capabilities. Not only is improved wireless performance expected with traditional devices such as mobile phones, but new use cases such as the internet-of-things and autonomous vehicle operation will rely on 5G and future network generations. In such widespread applications, from transportation to vital business operation, reliable and often guaranteed connectivity is required for safety and commercial approval. Introducing UAVs into network processes has been explored and implemented in certain instances to take advantage of the flexibility drone devices offer in their mobility and control to address these evolving network possibilities. While practical UAV deployment in certain network cases has been demonstrated, including coverage restoration in disaster relief scenarios, more ambitious goals of 5G will have additional considerations. This includes autonomous vehicles (AVs) whose operation is defined by levels representing varying degrees of autonomy. With computational requirements exponentially increasing as a vehicle's autonomy level is increased, 5G is expected to play an integral role in offloading certain vehicle tasks to the cloud. This thesis then proposes UAV based measurement opportunities as a method to characterize 5G coverage as part of autonomous vehicle processes to identify the proper level of autonomy that can operate safely given the current RF environment. This thesis proposes an UAV based measurement system that would provide coverage verification employing a platform capable of precise RF measurements and enhanced spatial sampling of the environment. Methods employed to traditionally characterize available coverage, including cellular drive tests, do not result in accurate enough measurements for AV use cases. Where lack of coverage in common network processes and use cases can result in dropped calls and poor connectivity in mobile devices, autonomous systems proposed in evolving network generations that deal with safety and mission critical functions must have guaranteed and verified coverage. Data produced in this thesis demonstrates that the proposed UAV based measurement system will improve measurement accuracy and enhanced geographic performance over conventional automotive vehicle based measurement systems / Master of Science / Wireless networks have grown to support vital and everyday processes in modern society. The COVID-19 pandemic proved wireless communication means a necessity to limit daily disruptions, but networks had already been supporting a continuously increasing amount of mobile devices prior to this. Other demonstrations of wireless network capacity include the release of 5G technology, allowing improved performance with traditional devices like smartphones, along with additional use cases this technology enables including the internet-of-things (IoT) and artificial intelligence (AI) leveraged functions for commercial applications. While wireless network capabilities have demonstrated their success in supporting and maintaining some critical functions, it is important to continually look ahead and plan for future network implementations in order to develop and support all desired advancements. Current measurement methods that assist in verifying coverage for current use cases like mobile devices will fall short in verification for more stringent requirements characteristic of AV and other ambitious network goals. The results found in this work then support the need for continuing research of a UAV-leveraged platform in the scope of eventual practical and safe AV integration into society. The focus of this thesis is to then propose and provide initial evaluation of a UAV-leveraged measurement platform to verify the operability of autonomous vehicles (AVs), which are expected to be a major aspect of future network processes. The computational requirements to operate an autonomous vehicle exponentially increase as a vehicle's autonomy level is increased. 5G is then expected to play an integral role in offloading certain vehicle tasks to the cloud. This thesis paper then proposes UAV based measurement opportunities as a method to characterize 5G coverage as part of autonomous vehicle processes to identify the proper level of autonomy that can operate safely given the current RF environment.

<p dir="ltr">Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians</p><p dir="ltr">and vehicles) to make optimal decisions for navigation. The existing methods focus on</p><p dir="ltr">techniques to utilize the positions and velocities of these agents and fail to capture semantic</p><p dir="ltr">information from the scene. Moreover, to mitigate the increase in computational complexity</p><p dir="ltr">associated with the number of agents in the scene, some works leverage Euclidean distance to</p><p dir="ltr">prune far-away agents. However, distance-based metric alone is insufficient to select relevant</p><p dir="ltr">agents and accurately perform their predictions. To resolve these issues, we propose the</p><p dir="ltr">Semantics-aware Interactive Multiagent Motion Forecasting (SIMMF) method to capture</p><p dir="ltr">semantics along with spatial information and optimally select relevant agents for motion</p><p dir="ltr">prediction. Specifically, we achieve this by implementing a semantic-aware selection of relevant</p><p dir="ltr">agents from the scene and passing them through an attention mechanism to extract</p><p dir="ltr">global encodings. These encodings along with agents’ local information, are passed through</p><p dir="ltr">an encoder to obtain time-dependent latent variables for a motion policy predicting the future</p><p dir="ltr">trajectories. Our results show that the proposed approach outperforms state-of-the-art</p><p dir="ltr">baselines and provides more accurate and scene-consistent predictions. </p>

Autonomous driving requires tactical decision-making while navigating in a dynamic shared space environment. The complexity and uncertainty in this process arise due to unknown and tightly-coupled interaction among traffic users. This thesis work formulates an unknown navigation problem as a Markov decision process (MDP), supported by models of traffic participants and userspace. Instead of modeling a traditional MDP, this work formulates a Multi-policy decision making (MPDM) in a shared space scenario with pedestrians and vehicles. The employed model enables a unified and robust self-driving of the ego vehicle by selecting a desired policy along the pre-planned path. Obstacle avoidance is coupled within the navigation module performing a detour off the planned path and obtaining a reward on task completion and penalizing for collision with others. In addition to this, the thesis work is further extended by analyzing the real-time constraints of the proposed model. The performance of the implemented framework is evaluated in a simulation environment on a typical construction (quarry) scenario. The effectiveness and efficiency of the elected policy verify the desired behavior of the autonomous vehicle.

Optimizing city logistics is a topic of great importance for big cities today. Even though the environmental- and economical consequences of the increasing goods transportation fleet has been present for quite some time, actions from affected stakeholders has not been taken until just recently.  In this brief 9 week project the aim has been to,  from a design point-of-view, in this complex topic, give a suggestion and raise a discussion onto how goods transportation in cities can be made in the future.  The basic idea is to get the consignments as close to the consignees and their travel patterns as possible. Public transportation networks are an existing grid where people are moving around and can potentially be used for combined transport of goods and people. The outcome of this concept is focusing on light parcel delivery towards private consignees, due to the increasing e-commerce trend. By using small transporter vehicles connected to buses, light parcels can be distributed throughout a city and easily accessible for consignees at the bus stops.  The transporter vehicle developed through this project aims to conceptualize connectivity and integration between mobility and parcel delivery services, to ease the every day life of urban citizens as well as decrease the environmental- and economical impacts which the distribution vehicle-fleet of today entails.  The design of the transporter vehicle is an interpretation of how a autonomous distribution vehicle can look like in a future city environment where the integration of public-utility-vehicles is of greater importance.

Os equipamentos robóticos foram inicialmente criados para atuarem em ambientes industriais fechados. Com o passar do tempo, melhorias foram conquistadas. Atualmente, não se limitam mais à realização de tarefas simples e repetitivas em locais especialmente preparados. Novos equipamentos, capazes de atuarem em ambientes abertos e de realizarem as mais diversas atividades, estão sendo desenvolvidos. Para tanto, é necessário que seus sistemas de controle realizem uma efetiva interação com o mundo onde estão inseridos. Fazem-se necessários, portanto, novos sistemas controladores com capacidade de uma contínua adaptação ao ambiente dinâmico onde operam. As redes neurais artificiais, devido a sua capacidade de tratamento de problemas não lineares – matematicamente difíceis de serem resolvidos, estão sendo empregadas no controle destes processos. O gerenciamento da trajetória de um veículo móvel em ambientes abertos ou fechados é um procedimento altamente não-linear, logo, a aplicação das redes neurais artificiais é bastante promissora. Apesar de sua grande versatilidade, as redes neurais artificiais têm sido utilizadas apenas como sistemas de mapeamento. A grande maioria delas necessita de uma fase de treinamento para que possam armazenar a diversidade de estados possíveis do sistema. Quando atuam, elas simplesmente mapeiam os seus valores de entrada (estado atual) nas soluções previamente armazenadas. Contudo, esta não é a melhor abordagem para os sistemas abertos, ou seja, para os processos cujas situações e possibilidades não podem ser totalmente enumeradas e que podem ser mutáveis no decorrer do tempo. Este trabalho apresenta uma metodologia de controle neural adaptativo para guiar um veículo móvel até o seu destino em ambientes contendo obstáculos fixos ou móveis. Diferentemente das abordagens tradicionais, não existe a necessidade de um treinamento prévio da rede. A rede neural artificial escolhida promove uma contínua adaptação do sistema enquanto atua. Neste processo, são utilizados sensores que fornecem subsídios para que a rede possa gerar, adaptativamente, soluções parciais que façam com que o veículo autônomo se aproxime cada vez mais do seu objetivo, até, finalmente, atingi-lo. / The robotic equipments were created initially to actuate in closed industrial environments. Improvements have been acquieved in this area. Nowadays, they are no longer limited to perform simple and repetitive tasks in controlled places. New equipments, capable of acting in open environments and doing the most several activities, are being developed. For so much, it is necessary that its control systems accomplish an effective interaction with the world where they are inserted. Therefore, new systems controllers with capacity of a continuous adaptation to the dynamic environments are essential. Artificial neural networks, due to their capacity of dealing wit non-linear problems – mathematically difficult to be solved – are being used to control these kind of processes. Guide a mobile vehicle through an open or controlled environments is a highly non-linear procedure; therefore, the use of an artificial neural nets is quite promising. In spite of its great versatility, they have just been used as mapping systems. Most of them need a training phase so that they can store the diversity of system’s possible states. When they actuate, they simply map their input values (current state) to the solutions previously stored. However, this is not the best approach for open systems, i.e. systems whose situations and possibilities cannot be totally enumerated and that can change in time. This work presents an adaptive neural control methodology to guide a mobile vehicle to its target in environments with fixed or mobile obstacles. Differently from the traditional approaches, the need of a previous training phase of the neural network doesn't exist. The chosen model of artificial neural net promotes a continuous adaptation of the system while it actuates. Sensors are used to provide informations to the net. This way it generates partial solutions that makes the autonomous vehicle gets closer of its goal, until, finally, reach it.

Os equipamentos robóticos foram inicialmente criados para atuarem em ambientes industriais fechados. Com o passar do tempo, melhorias foram conquistadas. Atualmente, não se limitam mais à realização de tarefas simples e repetitivas em locais especialmente preparados. Novos equipamentos, capazes de atuarem em ambientes abertos e de realizarem as mais diversas atividades, estão sendo desenvolvidos. Para tanto, é necessário que seus sistemas de controle realizem uma efetiva interação com o mundo onde estão inseridos. Fazem-se necessários, portanto, novos sistemas controladores com capacidade de uma contínua adaptação ao ambiente dinâmico onde operam. As redes neurais artificiais, devido a sua capacidade de tratamento de problemas não lineares – matematicamente difíceis de serem resolvidos, estão sendo empregadas no controle destes processos. O gerenciamento da trajetória de um veículo móvel em ambientes abertos ou fechados é um procedimento altamente não-linear, logo, a aplicação das redes neurais artificiais é bastante promissora. Apesar de sua grande versatilidade, as redes neurais artificiais têm sido utilizadas apenas como sistemas de mapeamento. A grande maioria delas necessita de uma fase de treinamento para que possam armazenar a diversidade de estados possíveis do sistema. Quando atuam, elas simplesmente mapeiam os seus valores de entrada (estado atual) nas soluções previamente armazenadas. Contudo, esta não é a melhor abordagem para os sistemas abertos, ou seja, para os processos cujas situações e possibilidades não podem ser totalmente enumeradas e que podem ser mutáveis no decorrer do tempo. Este trabalho apresenta uma metodologia de controle neural adaptativo para guiar um veículo móvel até o seu destino em ambientes contendo obstáculos fixos ou móveis. Diferentemente das abordagens tradicionais, não existe a necessidade de um treinamento prévio da rede. A rede neural artificial escolhida promove uma contínua adaptação do sistema enquanto atua. Neste processo, são utilizados sensores que fornecem subsídios para que a rede possa gerar, adaptativamente, soluções parciais que façam com que o veículo autônomo se aproxime cada vez mais do seu objetivo, até, finalmente, atingi-lo. / The robotic equipments were created initially to actuate in closed industrial environments. Improvements have been acquieved in this area. Nowadays, they are no longer limited to perform simple and repetitive tasks in controlled places. New equipments, capable of acting in open environments and doing the most several activities, are being developed. For so much, it is necessary that its control systems accomplish an effective interaction with the world where they are inserted. Therefore, new systems controllers with capacity of a continuous adaptation to the dynamic environments are essential. Artificial neural networks, due to their capacity of dealing wit non-linear problems – mathematically difficult to be solved – are being used to control these kind of processes. Guide a mobile vehicle through an open or controlled environments is a highly non-linear procedure; therefore, the use of an artificial neural nets is quite promising. In spite of its great versatility, they have just been used as mapping systems. Most of them need a training phase so that they can store the diversity of system’s possible states. When they actuate, they simply map their input values (current state) to the solutions previously stored. However, this is not the best approach for open systems, i.e. systems whose situations and possibilities cannot be totally enumerated and that can change in time. This work presents an adaptive neural control methodology to guide a mobile vehicle to its target in environments with fixed or mobile obstacles. Differently from the traditional approaches, the need of a previous training phase of the neural network doesn't exist. The chosen model of artificial neural net promotes a continuous adaptation of the system while it actuates. Sensors are used to provide informations to the net. This way it generates partial solutions that makes the autonomous vehicle gets closer of its goal, until, finally, reach it.

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion.

Os equipamentos robóticos foram inicialmente criados para atuarem em ambientes industriais fechados. Com o passar do tempo, melhorias foram conquistadas. Atualmente, não se limitam mais à realização de tarefas simples e repetitivas em locais especialmente preparados. Novos equipamentos, capazes de atuarem em ambientes abertos e de realizarem as mais diversas atividades, estão sendo desenvolvidos. Para tanto, é necessário que seus sistemas de controle realizem uma efetiva interação com o mundo onde estão inseridos. Fazem-se necessários, portanto, novos sistemas controladores com capacidade de uma contínua adaptação ao ambiente dinâmico onde operam. As redes neurais artificiais, devido a sua capacidade de tratamento de problemas não lineares – matematicamente difíceis de serem resolvidos, estão sendo empregadas no controle destes processos. O gerenciamento da trajetória de um veículo móvel em ambientes abertos ou fechados é um procedimento altamente não-linear, logo, a aplicação das redes neurais artificiais é bastante promissora. Apesar de sua grande versatilidade, as redes neurais artificiais têm sido utilizadas apenas como sistemas de mapeamento. A grande maioria delas necessita de uma fase de treinamento para que possam armazenar a diversidade de estados possíveis do sistema. Quando atuam, elas simplesmente mapeiam os seus valores de entrada (estado atual) nas soluções previamente armazenadas. Contudo, esta não é a melhor abordagem para os sistemas abertos, ou seja, para os processos cujas situações e possibilidades não podem ser totalmente enumeradas e que podem ser mutáveis no decorrer do tempo. Este trabalho apresenta uma metodologia de controle neural adaptativo para guiar um veículo móvel até o seu destino em ambientes contendo obstáculos fixos ou móveis. Diferentemente das abordagens tradicionais, não existe a necessidade de um treinamento prévio da rede. A rede neural artificial escolhida promove uma contínua adaptação do sistema enquanto atua. Neste processo, são utilizados sensores que fornecem subsídios para que a rede possa gerar, adaptativamente, soluções parciais que façam com que o veículo autônomo se aproxime cada vez mais do seu objetivo, até, finalmente, atingi-lo. / The robotic equipments were created initially to actuate in closed industrial environments. Improvements have been acquieved in this area. Nowadays, they are no longer limited to perform simple and repetitive tasks in controlled places. New equipments, capable of acting in open environments and doing the most several activities, are being developed. For so much, it is necessary that its control systems accomplish an effective interaction with the world where they are inserted. Therefore, new systems controllers with capacity of a continuous adaptation to the dynamic environments are essential. Artificial neural networks, due to their capacity of dealing wit non-linear problems – mathematically difficult to be solved – are being used to control these kind of processes. Guide a mobile vehicle through an open or controlled environments is a highly non-linear procedure; therefore, the use of an artificial neural nets is quite promising. In spite of its great versatility, they have just been used as mapping systems. Most of them need a training phase so that they can store the diversity of system’s possible states. When they actuate, they simply map their input values (current state) to the solutions previously stored. However, this is not the best approach for open systems, i.e. systems whose situations and possibilities cannot be totally enumerated and that can change in time. This work presents an adaptive neural control methodology to guide a mobile vehicle to its target in environments with fixed or mobile obstacles. Differently from the traditional approaches, the need of a previous training phase of the neural network doesn't exist. The chosen model of artificial neural net promotes a continuous adaptation of the system while it actuates. Sensors are used to provide informations to the net. This way it generates partial solutions that makes the autonomous vehicle gets closer of its goal, until, finally, reach it.