The development of Automated Driving Systems (ADS) has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Functional Safety (FuSa) and Operational Safety (OpSa). These terms of FuSa and OpSa are important, because ADS testing relies on both. Current recommendations for ADS testing include both OpSa and FuSa requirements. However, an examination of ADS safety requirements (e.g., industry reports, post-crash analysis reports, etc.) reveals that ADS safety arguments, in practice, depend almost completely on well-trained human operators, referred to in the industry as in vehicle fallback test drivers (IFTD). To date, the industry has never fielded a truly SAE L4 ADS on public roads due to this persistent hurdle of needing a human operator for Operational Safety. There is a tendency in ADS testing to reference International Standards Organization (ISOs) for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, ISOs for ADS end products are not necessarily applicable to ADS in development. With this in mind, there is a clear gap in the industry for unvalidated ADS literature. Because of this gap, ADS testing for unvalidated vehicles often relies on safety requirements for validated vehicles. This issue remains a significant challenge for ADS testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the ADS industry to develop a clear strategy for transitioning from an IFTD (Operational Safety) to an ADS (Functional Safety). Therefore, the purpose of this thesis is to present a framework for transitioning from Operational Safety to Functional Safety. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages Functional and Operational Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the ADS industry. / Master of Science / The development of Self-Driving Cars has been ongoing for decades in varying levels of sophistication. Levels of automation are defined by Society of American Engineers (SAE) as 0 through 5, with 0 being full human control and 5 being full automation control. Another way to describe levels of automation is through concepts of Robotic Control and Human Control. If a vehicle relies completely on Human Control, a human operator is responsible for all on-road safety. On the other hand, a fully autonomous would be considered fully in Robotic Control. These terms of Robotic Control and Human Control are important, because Self-Driving Car testing relies on both. Current recommendations for Self-Driving Car testing include both Robotic Control and Human Control requirements. However, an examination of Self-Driving Cars documentation (e.g., industry reports, post-crash analysis reports, etc.) reveals that Self-Driving Car safety arguments, in practice, depend almost completely on well-trained human operators. To date, the industry has never fielded a truly SAE L4 Self-Driving Car on public roads due to this persistent hurdle of needing a human operator for Human Control. There is a tendency in Self-Driving Car testing to reference standars for validated vehicles for vehicles that are still in development (i.e., unvalidated). To be clear, standards for Self-Driving Car end products are not necessarily applicable to Self-Driving Cars in development. With this in mind, there is a clear gap in the industry for unvalidated Self-Driving Car literature. Because of this gap, Self-Driving Car testing for unvalidated vehicles often relies on documentation for validated vehicles. This issue remains a significant challenge for Self-Driving Car testing. Recognizing this gap in on-road, in-development vehicle safety, there is a need for the Self-Driving industry to develop a clear strategy for transitioning from Human Control to Robot Control. Therefore, the purpose of this thesis is to present a framework for transitioning from Human to Robot Control. The framework makes this possible through an inductive analysis of available definitions of onroad safety to arrive at a definition that leverages all definitions of Safety along a continuum. Ultimately, the framework aims to contribute to onroad safety testing for the Self-Driving industry.

The integration of autonomous vehicles into the food delivery sector represents a significant leap forward in enhancing efficiency, reducing human labor, and potentially lowering costs. However, their deployment faces significant challenges, including security and data integrity, compounded by the limitations of traditional blockchain technologies such as high energy demands and slow transaction processing that hinder scalability and real-time operations. This paper proposes the integration of IOTA blockchain with autonomous delivery vehicles to address these issues. IOTA's Tangle, a Directed Acyclic Graph, offers transaction fee elimination, reduced energy consumption, and improved scalability with quicker confirmations, aligning with the needs of the Internet of Things (IoT) and autonomous delivery systems. The research indicates that IOTA's integration significantly boosts the operational efficiency, security, and scalability of autonomous food delivery robots, supports seamless micropayments, and upholds data integrity, facilitating a decentralized, self-sufficient delivery ecosystem. These findings not only enhance current delivery services but also signal a shift towards broader applications in various sectors, laying the groundwork for extensive IOTA blockchain adoption in IoT, marking a step towards a new era of streamlined, secure, and scalable delivery services.

This study addresses the challenge of lens obfuscations in off-road autonomous vehicles, which compromise the essential visual inputs for safe navigation. Using a tiered approach, the research employs neural network architectures for preliminary image classification, semantic segmentation, and image-to-image translation to rectify obscured visual inputs. Initial classification using MobileNetV2 sets the stage for U-Net-driven semantic segmentation to identify obfuscated regions, followed by a modified Pix-to-Pix model for image restoration. The evaluation showcases promising results in improving visual clarity, marking a significant stride towards enhancing autonomous vehicle operational robustness in off-road environments. This work lays a foundation for future explorations into advanced neural network architectures for real-time implementations in off-road terrains.

The development of active safety systems in vehicles is a complex task and requires rigorous and precise testing. This testing needs to be done both by scripted/Automated tests such as HIL (Hardware in loop) and real life in target system validation. This thesis presents modern visualization tools to manage and analyze relevant test data. The project began with a pre-study phase, where interviews were conducted to elicit requirements for such a system. These were then applied over two iterations of development of both a low- and high-fidelity prototype. The end result was Theia, an easy-to-use tool to evaluate the performance of cameras and sensors with recorded vehicle data. Theia was then evaluated through a series of user tests, which concluded that it was an effective way to design such a system. Furthermore, the strategy to use for developing a modern such system, and how to prioritize relevant visualization data were also formulated.

Contemporary research indicates that the era of autonomous vehicles (AVs) is not only inevitable but may be reached sooner than expected; however, not enough research has been done to address road infrastructure readiness for supporting AV operations. Highway agencies at all levels of governments seek to identify the needed infrastructure changes to facilitate the successful integration of AVs into the existing roadway system. Given multiple sources of uncertainty particularly the market penetration of AVs, agencies find it difficult to justify the substantial investments needed to make these infrastructure changes using traditional value engineering approaches. It is needed to account for these uncertainties by doing a phased retrofitting of road infrastructure to keep up with the AV market penetration. This way, the agency can expand, defer, or scale back the investments at a future time. This dissertation develops a real options analysis (ROA) framework to address these issues while capturing the monetary value of investment timing flexibility. Using key stakeholder feedback, an extensive literature review, and discussions with experts, the needed AV-motivated changes in road infrastructure were identified across two stages of AV operations; the transition phase and the fully-autonomous phase. For a project-level case study of a 66-mile stretch of Indiana’s four-six lane Interstate corridor, two potential scenarios of infrastructure retrofitting were established and evaluated using the net present value (NPV) and ROA approaches. The results show that the NPV approach can lead to decisions at the start of the evaluation period but does not address the uncertainty associated with AV market penetration. In contrast, ROA was found to address uncertainty by incorporating investment timing flexibility and capturing its monetary value. Using the dissertation’s framework, agencies can identify and analyze a wide range of possible scenarios of AV-oriented infrastructure retrofitting to enhance readiness, at both the project and network levels.

Veículo autônomo inteligente (ou apenas veículo autônomo VA) é um tipo de sistema embarcado que integra componentes físicos (hardware) e computacionais (software). Sua principal característica é a capacidade de locomoção e de operação de modo semi ou completamente autônomo. A autonomia cresce com a capacidade de percepção e de deslocamento no ambiente, robustez e capacidade de resolver e executar tarefas lidando com as mais diversas situações (inteligência). Veículos autônomos representam um tópico de pesquisa importante e que tem impacto direto na sociedade. No entanto, à medida que esse campo avança alguns problemas secundários aparecem como, por exemplo, como saber se esses sistemas foram suficientemente testados. Uma das fases do teste de um VA é o teste de campo, em que o veículo é levado para um ambiente pouco controlado e deve executar livremente a missão para a qual foi programado. Ele é geralmente utilizado para garantir que os veículos autônomos mostrem o comportamento desejado, mas nenhuma informação sobre a estrutura do código é utilizada. Pode ocorrer que o veículo (hardware e software) passou no teste de campo, mas trechos importantes do código nunca tenham sido executados. Durante o teste de campo, os dados de entrada são coletados em logs que podem ser posteriormente analisados para avaliar os resultados do teste e para realizar outros tipos de teste offline. Esta tese apresenta um conjunto de propostas para apoiar a análise do teste de campo do ponto de vista do teste estrutural. A abordagem é composta por um modelo de classes no contexto do teste de campo, uma ferramenta que implementa esse modelo e um algoritmo genético para geração de dados de teste. Apresenta também heurísticas para reduzir o conjunto de dados contidos em um log sem diminuir substancialmente a cobertura obtida e estratégias de combinação e mutação que são usadas no algoritmo. Estudos de caso foram conduzidos para avaliar as heurísticas e estratégias e são também apresentados e discutidos. / Intelligent autonomous vehicle (or just autonomous vehicle - AV) is a type of embedded system that integrates physical (hardware) and computational (software) components. Its main feature is the ability to move and operate partially or fully autonomously. Autonomy grows with the ability to perceive and move within the environment, robustness and ability to solve and perform tasks dealing with different situations (intelligence). Autonomous vehicles represent an important research topic that has a direct impact on society. However, as this field progresses some secondary problems arise, such as how to know if these systems have been sufficiently tested. One of the testing phases of an AV is the field testing, where the vehicle is taken to a controlled environment and it should execute the mission for which it was programed freely. It is generally used to ensure that autonomous vehicles show the intended behavior, but it usually does not take into consideration the code structure. The vehicle (hardware and software) could pass the field testing, but important parts of the code may never have been executed. During the field testing, the input data are collected in logs that can be further analyzed to evaluate the test results and to perform other types of offline tests. This thesis presents a set of proposals to support the analysis of field testing from the point of view of the structural testing. The approach is composed of a class model in the context of the field testing, a tool that implements this model and a genetic algorithm to generate test data. It also shows heuristics to reduce the data set contained in a log without reducing substantially the coverage obtained and combination and mutation strategies that are used in the algorithm. Case studies have been conducted to evaluate the heuristics and strategies, and are also presented and discussed.

Veículo autônomo inteligente (ou apenas veículo autônomo VA) é um tipo de sistema embarcado que integra componentes físicos (hardware) e computacionais (software). Sua principal característica é a capacidade de locomoção e de operação de modo semi ou completamente autônomo. A autonomia cresce com a capacidade de percepção e de deslocamento no ambiente, robustez e capacidade de resolver e executar tarefas lidando com as mais diversas situações (inteligência). Veículos autônomos representam um tópico de pesquisa importante e que tem impacto direto na sociedade. No entanto, à medida que esse campo avança alguns problemas secundários aparecem como, por exemplo, como saber se esses sistemas foram suficientemente testados. Uma das fases do teste de um VA é o teste de campo, em que o veículo é levado para um ambiente pouco controlado e deve executar livremente a missão para a qual foi programado. Ele é geralmente utilizado para garantir que os veículos autônomos mostrem o comportamento desejado, mas nenhuma informação sobre a estrutura do código é utilizada. Pode ocorrer que o veículo (hardware e software) passou no teste de campo, mas trechos importantes do código nunca tenham sido executados. Durante o teste de campo, os dados de entrada são coletados em logs que podem ser posteriormente analisados para avaliar os resultados do teste e para realizar outros tipos de teste offline. Esta tese apresenta um conjunto de propostas para apoiar a análise do teste de campo do ponto de vista do teste estrutural. A abordagem é composta por um modelo de classes no contexto do teste de campo, uma ferramenta que implementa esse modelo e um algoritmo genético para geração de dados de teste. Apresenta também heurísticas para reduzir o conjunto de dados contidos em um log sem diminuir substancialmente a cobertura obtida e estratégias de combinação e mutação que são usadas no algoritmo. Estudos de caso foram conduzidos para avaliar as heurísticas e estratégias e são também apresentados e discutidos. / Intelligent autonomous vehicle (or just autonomous vehicle - AV) is a type of embedded system that integrates physical (hardware) and computational (software) components. Its main feature is the ability to move and operate partially or fully autonomously. Autonomy grows with the ability to perceive and move within the environment, robustness and ability to solve and perform tasks dealing with different situations (intelligence). Autonomous vehicles represent an important research topic that has a direct impact on society. However, as this field progresses some secondary problems arise, such as how to know if these systems have been sufficiently tested. One of the testing phases of an AV is the field testing, where the vehicle is taken to a controlled environment and it should execute the mission for which it was programed freely. It is generally used to ensure that autonomous vehicles show the intended behavior, but it usually does not take into consideration the code structure. The vehicle (hardware and software) could pass the field testing, but important parts of the code may never have been executed. During the field testing, the input data are collected in logs that can be further analyzed to evaluate the test results and to perform other types of offline tests. This thesis presents a set of proposals to support the analysis of field testing from the point of view of the structural testing. The approach is composed of a class model in the context of the field testing, a tool that implements this model and a genetic algorithm to generate test data. It also shows heuristics to reduce the data set contained in a log without reducing substantially the coverage obtained and combination and mutation strategies that are used in the algorithm. Case studies have been conducted to evaluate the heuristics and strategies, and are also presented and discussed.

No description available.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The Edwards Flight Test Range is a part of 20,000 square miles of DOD airspace (R-2508). A hypersonic air vehicle traveling above Mach 3 can easily exceed that airspace within seconds. An Unpiloted Autonomous Vehicle can exceed the airspace when flying long duration missions. To satisfy the flight-test requirements of Hypersonic Air Vehicles and Unpiloted Autonomous Vehicles, additional airspace and extended test ranges are required. The Air Force Flight Test Center and Dryden Flight Research Center at Edwards Air Force Base, California have mutual goals to support these flight test programs. To meet these goals, the Extended Test Range Alliance was formed as an engineering and operations team to satisfy program requirements in the areas of telemetry, flight termination, ground communications, uplink command, and differential global positioning systems. This paper will discuss the resources and technical capabilities available through the Extended Test Range.

Thesis (PhD)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: A strategy for the design of an effective, practically feasible, robust, computationally efficient autopilot for three dimensional manoeuvre flight control of Unmanned Aerial Vehicles is presented. The core feature of the strategy is the design of attitude independent inner loop acceleration controllers. With these controllers implemented, the aircraft is reduced to a point mass with a steerable acceleration vector when viewed from an outer loop guidance perspective. Trajectory generation is also simplified with reference trajectories only required to be kinematically feasible. Robustness is achieved through uncertainty encapsulation and disturbance rejection at an acceleration level. The detailed design and associated analysis of the inner loop acceleration controllers is carried out for the case where the airflow incidence angles are small. For this case it is shown that under mild practically feasible conditions the inner loop dynamics decouple and become linear, thereby allowing the derivation of closed form pole placement solutions. Dimensional and normalised non-dimensional time variants of the inner loop controllers are designed and their respective advantages highlighted. Pole placement constraints that arise due to the typically weak non-minimum phase nature of aircraft dynamics are developed. A generic, aircraft independent guidance control algorithm, well suited for use with the inner loop acceleration controllers, is also presented. The guidance algorithm regulates the aircraft about a kinematically feasible reference trajectory. A number of fundamental basis trajectories are presented which are easily linkable to form complex three dimensional manoeuvres. Results from simulations with a number of different aircraft and reference trajectories illustrate the versatility and functionality of the autopilot. Key words: Aircraft control, Autonomous vehicles, UAV flight control, Acceleration control, Aircraft guidance, Trajectory tracking, Manoeuvre flight control. / AFRIKAANSE OPSOMMING: ’n Strategie vir die ontwerp van ’n effektiewe, prakties haalbaar, robuuste, rekenkundig effektiewe outoloods vir drie dimensionele maneuver vlugbeheer van onbemande vliegtuie word voorgestel. Die kerneienskap van die strategie is die ontwerp van oriëntasie-onafhanklike binnelus-versnellingbeheerders. Hierdie beheerders stel die navigasie buitelus in staat om die voertuig as ’n puntmassa met ’n stuurbare versnellingsvektor te beskou. Trajekgenerasie is ook vereenvoudig deurdat verwysingstrajekte slegs kinematies haalbaar hoef te wees. Robuustheid word verkry deur onsekerhede en versteuringsverwerping op ’n versnellingsvlak te hanteer. Die gedetaileerde ontwerp en saamhangende analise van die binnelus versnellingsbeheerders word uitgevoer vir die geval waar die invalshoeke klein is. Dit word aangetoon dat, onder praktiese omstandighede, die binnelus dinamika ontkoppel kan word en lineêr word, wat die afleiding van geslotevorm poolplasingoplossings toelaat. Dimensionele en genormaliseerde, nie-dimensionele tydvariante van die binnelusbeheerders word ontwerp en hul onderskeidelike voordele word uitgewys. Poolplasing beperkings, wat ontstaan as gevolg van die tipiese geringe nie-minimum fasegedrag van voertuigdinamika, word ontwikkel. ’n Gepaste generiese, voertuig onafhanklike navigasiebeheer algoritme vir gebruik saam met die binnelus-versnellingsbeheerders word voorgestel. Die voertuig word om ’n kinematies haalbare verwysingstrajek deur hierdie navigasie algoritme gereguleer. ’n Aantal fundamentele trajekte word voorgestel wat maklik gekombineer kan word om komplekse drie dimensionele maneuvers te vorm. Die veelsydigheid en funksionaliteit van die outoloods word deur simulasieresultate met ’n verskeidenheid voertuie en verwysingstrajekte gedemonstreer.