A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution.This research was funded by AutoDrive within the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement number 737469

An Empirical Study on V2X Enhanced Low-Cost GNSS Cooperative Positioning in Urban Environments

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm&rsquo s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments. Document type: Articl

Visual-Inertial State Estimation With Information Deficiency

State estimation is an essential part of intelligent navigation and mapping systems where tracking the location of a smartphone, car, robot, or a human-worn device is required. For autonomous systems such as micro aerial vehicles and self-driving cars, it is a prerequisite for control and motion planning. For AR/VR applications, it is the first step to image rendering. Visual-inertial odometry (VIO) is the de-facto standard algorithm for embedded platforms because it lends itself to lightweight sensors and processors, and maturity in research and industrial development. Various approaches have been proposed to achieve accurate real-time tracking, and numerous open-source software and datasets are available. However, errors and outliers are common due to the complexity of visual measurement processes and environmental changes, and in practice, estimation drift is inevitable. In this thesis, we introduce the concept of information deficiency in state estimation and how to utilize this concept to develop and improve VIO systems. We look into the information deficiencies in visual-inertial state estimation, which are often present and ignored, causing system failures and drift. In particular, we investigate three critical cases of information deficiency in visual-inertial odometry: low texture environment with limited computation, monocular visual odometry, and inertial odometry. We consider these systems under three specific application settings: a lightweight quadrotor platform in autonomous flight, driving scenarios, and AR/VR headset for pedestrians. We address the challenges in each application setting and explore how the tight fusion of deep learning and model-based VIO can improve the state-of-the-art system performance and compensate for the lack of information in real-time. We identify deep learning as a key technology in tackling the information deficiencies in state estimation. We argue that developing hybrid frameworks that leverage its advantage and enable supervision for performance guarantee provides the most accurate and robust solution to state estimation

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

AI and IoT Meet Mobile Machines

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

Differential global positioning system (DGPS) for flight testing

In this volume, the potential of Differential Global Positioning System (DGPS) as a positioning datum for flight test applications is thoroughly discussed. Current technology status and future trends are investigated to identify optimal system architectures for both the on-board and ground station components, and to define optimal strategies for DGPS data gathering during various flight testing tasks. Limitations of DGPS techniques are analyzed, and various possible integration schemes with other sensors are considered. Finally, the optimal architecture of an integrated position reference system suitable for a variety of flight test applications is identified. This volume provides comprehensive guidance on assessing the need for and determining the characteristics of DGPS based position reference systems for flight test activities. The specific goals are to make available to the NATO flight test community the best practices and advice for DGPS based systems architecture definition and equipment selection. A variety of flight test applications are examined and both real-time and post-mission DGPS data requirements are outlined. Particularly, DGPS accuracy, continuity and integrity issues are considered, and possible improvements achievable by means of signal augmentation strategies are identified. Possible architectures for integrating DGPS with other airborne sensors (e.g., Inertial Navigation, Radar Altimeter) are presented, with particular emphasis on current and likely future data fusion algorithms. Particular attention is devoted to simulation analysis in support of flight test activities. Finally, an outline of current research perspectives in the field of DGPS technology is given

Design Development Test and Evaluation (DDT and E) Considerations for Safe and Reliable Human Rated Spacecraft Systems

A team directed by the NASA Engineering and Safety Center (NESC) collected methodologies for how best to develop safe and reliable human rated systems and how to identify the drivers that provide the basis for assessing safety and reliability. The team also identified techniques, methodologies, and best practices to assure that NASA can develop safe and reliable human rated systems. The results are drawn from a wide variety of resources, from experts involved with the space program since its inception to the best-practices espoused in contemporary engineering doctrine. This report focuses on safety and reliability considerations and does not duplicate or update any existing references. Neither does it intend to replace existing standards and policy