This thesis describes the design of an inexpensive autonomous vehicle system using a small scaled model vehicle. The system is capable of operating in two different modes: telerobotic manual mode and automated driving mode. In telerobotic manual mode, the model vehicle is controlled by a human driver at a stationary remote control station with full-scale steering wheel and gas pedal. The vehicle can either be an unmodified toy remote-control car or a vehicle equipped with wireless radio modem for communication and microcontroller for speed control. In both cases the vehicle also carries a video camera capable of transmitting video images back to the remote control station where they are displayed on a monitor. In automated driving mode, the vehicle's lateral movement is controlled by a lateral control algorithm. The objective of this algorithm is to keep the vehicle in the center of a road. Position and orientation of the vehicle are determined by an image processing algorithm identifying a white middle marker on the road. Two different algorithm for image processing have been designed: one based on the pixel intensity profile and the other on vanishing points in the image plane. For the control algorithm itself, two designs are introduced as well: a simple classical P-control and a control scheme based on H-Infinity. The design and testing of this autonomous vehicle system are performed in the Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at Virginia Tech. / Master of Science

Despite the advancements in the development of autonomous vehicles (AVs), there are still numerous complex situations in which AVs may encounter challenges. In recent years, the concept of teleoperation, which entails establishing a connection between a remote operator and the AV, has garnered substantial attention from both AV companies and governmental bodies as a viable safety backup method. However, a research gap is apparent when it comes to the remote manipulation of AVs positioned at a considerable distance. This gap involves a) AV with a temporal delay through real-time direct control within the constraints of current wireless communication technology in an unpredictable road environment, and b) enhancing the AV's inherent detection capabilities to augment its autonomous control abilities, thereby reducing the operator's workload. To address this research gap, this dissertation introduces an innovative teleoperation system. Initially, we devise a control system utilizing the wave variable approach as a communication method to alleviate the impact of signal latency. And Radial Basis Function Networks (RBFN) are employed to effectively manage the uncertain nonlinear dynamics of the vehicle. Subsequently, a saliency-based object detection (OD) algorithm, named SalienDet, is proposed to identify objects not present in the training sample set. SalienDet incorporates saliency maps generated without prior information into the neural network, enhancing image features for unfamiliar objects. This augmentation significantly aids the OD algorithm in detecting previously unknown objects, thereby empowering the AV to possess an improved perception ability. This advancement is particularly valuable when the operator imparts driving advice to the AV instead of exercising direct control. In conclusion, this dissertation makes a noteworthy contribution to AV teleoperation by furnishing a comprehensive solution that spans various aspects of AV teleoperation. / Doctor of Philosophy / This dissertation revolves around the teleoperation of autonomous vehicles (AVs), with the objective of formulating a comprehensive teleoperation system that encompasses two critical aspects: direct control and indirect control. In the initial segment of the dissertation, we introduce a real-time teleoperation direct control system based on neural networks. This framework plays a pivotal role in assisting operators in navigating AVs efficiently, especially in the face of challenges such as communication delays and complex external environments. Following this, we present a novel saliency-based object detection (OD) algorithm. This algorithm empowers the AV to recognize potential objects beyond its prior knowledge, thereby enhancing its level of autonomous control, particularly when operators opt not to exercise direct control over the remote AV. Our research findings delve into the essential facets of AV teleoperation. The developed teleoperation system serves as a valuable reference for future researchers and engineers dedicated to advancing autonomous vehicle technology.

This manuscript includes an investigation of decision making in alternative modes of transportation in order to understand consumers' decision in different contexts. In essay 1 of this study, the motives for participation in situated ridesharing is investigated. The study proposes a theoretical model that includes economic benefits, time benefits, transportation anxiety, trust, and reciprocity either as direct antecedents of ridesharing participation intention, or mediated through attitude towards ridesharing. Essay 2 of this study, focuses on self-driving vehicles as one of the recent innovations in transportation industry. Using a survey approach, the study develops a conceptual model of consumers' anticipated motives. Both essays use partial least square- structural equation modeling for assessing the proposed theoretical models.

O desenvolvimento tecnológico e o elevado investimento em tecnologias de veículos \"inteligentes\" vão, provavelmente, transformar veículos autônomos em realidade em alguns anos. A inserção de inteligência em veículos rodoviários visa obter uma redução nos acidentes de trânsito devido à mitigação de erros cometidos por motoristas humanos, graças à sua substituição por máquinas. Além disso, os veículos autônomos devem ser capazes de mitigar os perigos existentes nos sistemas de transporte rodoviário, sem criar novos riscos. Assim, é importante a pesquisa de como garantir a segurança crítica (safety) nesse novo cenário. Algumas pesquisas nesta área já vêm sendo desenvolvidas, porém elas não mostram como projetar um sistema veicular autônomo no qual se possa aplicar métodos já existentes para analisar e garantir níveis de segurança adequados em tais veículos. Frente a isso, este trabalho de mestrado desenvolve uma proposta que visa facilitar o desenvolvimento e a análise dessa nova classe de veículos, além de assegurar níveis de segurança crítica adequados aos veículos autônomos. A proposta é representada por um sistema denominado Controle Veicular Autônomo (CVA), o qual foi desenvolvido sob o conceito de Sistemas de Transporte Inteligentes (STI). O sistema CVA é formado por duas camadas, uma de operação (Operação Veicular Autônoma - OVA), responsável pela condução do veículo e outra de proteção (Proteção Veicular Autônoma - PVA). A ideia principal é que se utilize a camada PVA para a prevenção de acidentes. A camada PVA foi desenvolvida e testada em um ambiente de simulação, considerando um Estudo de Caso. Observou-se que, conforme previsto, o sistema CVA, por possuir uma camada voltada para a proteção veicular, conseguiu evitar diversas situações de colisões entre veículos. / Technological development and the massive investment in \'intelligent\' vehicle technologies are likely to turn autonomous vehicles into reality in a few years. The insertion intelligence in road vehicles aims to obtain a reduction in traffic accidents due to the mitigation of errors committed by human drivers, thanks to their replacement by machines. In addition, autonomous vehicles should be able to mitigate hazards in road transportation systems without creating new risks. Thus, It is important to study how to ensure safety in this new scenario. Some research in this area has already been developed, but they do not show how to design properly an autonomous vehicle system in which existing methods can be applied to analyze and guarantee adequate levels of safety in such vehicles. As a result, this master\'s work develops a proposal that aims to facilitate the development and analysis of this new class of vehicles, in addition to ensuring levels of critical safety appropriate to autonomous vehicles. The proposal is represented by a system called Autonomous Vehicle Control (CVA), which was developed under the concept of Intelligent Transport Systems (STI). The CVA system is formed by two layers, one of operation (Autonomous Vehicle Operation - OVA), responsible for the driving of the vehicle and another one of protection (Autonomous Vehicle Protection - PVA). The main idea is to use the PVA layer for the prevention of accidents. The PVA layer was developed and tested in a simulation environment, considering a Case Study. It was observed that, as predicted, the CVA system, because it has a layer aimed at vehicular protection, was able to avoid several collision situations between vehicles.

O desenvolvimento tecnológico e o elevado investimento em tecnologias de veículos \"inteligentes\" vão, provavelmente, transformar veículos autônomos em realidade em alguns anos. A inserção de inteligência em veículos rodoviários visa obter uma redução nos acidentes de trânsito devido à mitigação de erros cometidos por motoristas humanos, graças à sua substituição por máquinas. Além disso, os veículos autônomos devem ser capazes de mitigar os perigos existentes nos sistemas de transporte rodoviário, sem criar novos riscos. Assim, é importante a pesquisa de como garantir a segurança crítica (safety) nesse novo cenário. Algumas pesquisas nesta área já vêm sendo desenvolvidas, porém elas não mostram como projetar um sistema veicular autônomo no qual se possa aplicar métodos já existentes para analisar e garantir níveis de segurança adequados em tais veículos. Frente a isso, este trabalho de mestrado desenvolve uma proposta que visa facilitar o desenvolvimento e a análise dessa nova classe de veículos, além de assegurar níveis de segurança crítica adequados aos veículos autônomos. A proposta é representada por um sistema denominado Controle Veicular Autônomo (CVA), o qual foi desenvolvido sob o conceito de Sistemas de Transporte Inteligentes (STI). O sistema CVA é formado por duas camadas, uma de operação (Operação Veicular Autônoma - OVA), responsável pela condução do veículo e outra de proteção (Proteção Veicular Autônoma - PVA). A ideia principal é que se utilize a camada PVA para a prevenção de acidentes. A camada PVA foi desenvolvida e testada em um ambiente de simulação, considerando um Estudo de Caso. Observou-se que, conforme previsto, o sistema CVA, por possuir uma camada voltada para a proteção veicular, conseguiu evitar diversas situações de colisões entre veículos. / Technological development and the massive investment in \'intelligent\' vehicle technologies are likely to turn autonomous vehicles into reality in a few years. The insertion intelligence in road vehicles aims to obtain a reduction in traffic accidents due to the mitigation of errors committed by human drivers, thanks to their replacement by machines. In addition, autonomous vehicles should be able to mitigate hazards in road transportation systems without creating new risks. Thus, It is important to study how to ensure safety in this new scenario. Some research in this area has already been developed, but they do not show how to design properly an autonomous vehicle system in which existing methods can be applied to analyze and guarantee adequate levels of safety in such vehicles. As a result, this master\'s work develops a proposal that aims to facilitate the development and analysis of this new class of vehicles, in addition to ensuring levels of critical safety appropriate to autonomous vehicles. The proposal is represented by a system called Autonomous Vehicle Control (CVA), which was developed under the concept of Intelligent Transport Systems (STI). The CVA system is formed by two layers, one of operation (Autonomous Vehicle Operation - OVA), responsible for the driving of the vehicle and another one of protection (Autonomous Vehicle Protection - PVA). The main idea is to use the PVA layer for the prevention of accidents. The PVA layer was developed and tested in a simulation environment, considering a Case Study. It was observed that, as predicted, the CVA system, because it has a layer aimed at vehicular protection, was able to avoid several collision situations between vehicles.

abstract: Lidar has demonstrated its utility in meteorological studies, wind resource assessment, and wind farm control. More recently, lidar has gained widespread attention for autonomous vehicles. The first part of the dissertation begins with an application of a coherent Doppler lidar to wind gust characterization for wind farm control. This application focuses on wind gusts on a scale from 100 m to 1000 m. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted. The second part of this dissertation describes a Time-of-Flight lidar simulation. The lidar simulation includes a laser source module, a propagation module, a receiver module, and a timing module. A two-dimensional pulse model is introduced in the laser source module. The sampling rate for the pulse model is explored. The propagation module takes accounts of beam divergence, target characteristics, atmosphere, and optics. The receiver module contains models of noise and analog filters in a lidar receiver. The effect of analog filters on the signal behavior was investigated. The timing module includes a Time-to-Digital Converter (TDC) module and an Analog-to-Digital converter (ADC) module. In the TDC module, several walk-error compensation methods for leading-edge detection and multiple timing algorithms were modeled and tested on simulated signals. In the ADC module, a benchmark (BM) timing algorithm is proposed. A Neyman-Pearson (NP) detector was implemented in the time domain and frequency domain (fast Fourier transform (FFT) approach). The FFT approach with frequency-domain zero-paddings improves the timing resolution. The BM algorithm was tested on simulated signals, and the NP detector was evaluated on both simulated signals and measurements from a prototype lidar (Bhaskaran, 2018). / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2019

Dans le cadre de cette thèse, nous nous intéressons à la coordination et l'optimisation du trafic aux intersections des réseaux routiers, avec la particularité de considérer des véhicules autonomes intelligents. Cette thèse est organisée en deux grandes parties. La première se concentre sur le problème du partage d'un espace de voie par deux files de véhicules évoluant en sens opposés. L'état de l'art montre le peu de travaux abordant cette question. Nous explorons deux approches par coordination réactive, en relation avec un critère de minimisation des retards. Les performances de ces approches ont été mesurées statistiquement en simulation. La deuxième partie de la thèse s'attaque au problème générique de la gestion du trafic au sein d'un réseau routier. Nous développons une approche originale à deux égards: d'une part elle explore un principe de passage en alternance des flux permettant de ne pas arrêter les véhicules aux intersections, et d'autre part, elle propose des algorithmes d'optimisationdistribuée de ce passage alterné au niveau de chaque intersection et au niveau du réseau global. La thèse présente successivement les choix de modélisation, les algorithmes et l'étude en simulation de leurs performances comparées à desapproches existantes / In this thesis, we focus on traffic coordination and optimization in road intersections, while accounting for intelligent autonomous vehicles. This thesis is organized in two parts. The first part focuses on the problem of sharing a one-lane road between two opposite flows of vehicles. The state of the art shows few studies addressing this issue. We propose two reactive coordination approaches that minimize vehicle delays and measure their performances statistically through simulations. The second part of the thesis addresses the problem of generic traffic management in a traffic network. We develop a stop-free approach that explores a principle alternating vehicles between flows at intersections, and it provides distributed algorithms optimizing this alternation at each intersection and in the overall network. We present the modeling choices, the algorithms and the simulation study of our approach and we compare its performances with existing approaches

Intelligence, surveillance, and reconnaissance unmanned aircraft systems (UAS) are the most common variety of UAS in use today and provide invaluable capabilities to both the military and civil services. Keeping the sensors centered on a point of interest for an extended period of time is a demanding task requiring the full attention and cooperation of the UAS pilot and sensor operator. There is great interest in developing technologies which allow an operator to designate a target and allow the aircraft to automatically maneuver and track the designated target without operator intervention. Presently, the barriers to entry for developing these technologies are high: expertise in aircraft dynamics and control as well as in real- time motion video analysis is required and the cost of the systems required to flight test these technologies is prohibitive. However, if the research intent is purely to develop a vehicle maneuvering controller then it is possible to obviate the video analysis problem entirely. This research presents a solution to the target tracking problem which reliably provides automatic target detection and tracking with low expense and computational overhead by making use of the infrared sensor from a Nintendo Wii Remote Controller.

Approved for public release; distribution is unlimited / Unmanned vehicle technology has matured significantly over the last two decades. This is evidenced by its widespread use in industrial and military applications ranging from deep-ocean exploration to anti-submarine warefare. Indeed, the feasiblity of short-range, special-purpose vehicles (whether aunonomous or remotely operated) is no longer in question. The research efforts have now begun to shift their focus on development of reliable, longer-range, high-endurance and fully autonomous systems. One of the major underlying technologies required to realize this goal is Artificial Intelligence (AI). The latter offers great potential to endow vehicles with the intelligence needed for full autonomy and extended range capability; this involves the increased application of AI technologies to support mission planning and execution, navigation and contingency planning. This thesis addresses two issues associated with the above goal for Autonomous Underwater Vehicles (AUV's). Firstly, a new approach is proposed for path planning in underwater environments that is capable of dealing with uncharted obstacles and which requires significantly less planning time and computer memory. Secondly, it explores the use of expert system technology in the planning of AUV missions.

abstract: Recently, automation, shared use, and electrification are proposed and viewed as the “three revolutions” in the future transportation sector to significantly relieve traffic congestion, reduce pollutant emissions, and increase transportation system sustainability. Motivated by the three revolutions, this research targets on the passenger-focused scheduled transportation systems, where (1) the public transit systems provide high-quality ridesharing schedules/services and (2) the upcoming optimal activity planning systems offer the best vehicle routing and assignment for household daily scheduled activities. The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range. In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition. The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018