Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches

The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architectures with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks. However, the lack of safety guarantees proves, which is one of the key challenges to be addressed, limit drastically the ambition to introduce more broadly AVs on our roads and restrict the use of AVs to very limited use cases. Therefore, the focus and the ambition of this paper is to survey research on autonomous vehicles while focusing on the important topic of safety guarantee of AVs. For this purpose, it is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs, with an emphasis on the safety assessment and decision-making systems composing these architectures. Moreover, it is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This is performed while emphasizing the strengths and weaknesses of each methodology and investigating the research that proposes a comprehensive multi-modal design that combines model-based and AI approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks

Modelling, Simulation and Data Analysis in Acoustical Problems

Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years

Multiple-objective sensor management and optimisation

One of the key challenges associated with exploiting modern Autonomous Vehicle technology for military surveillance tasks is the development of Sensor Management strategies which maximise the performance of the on-board Data-Fusion systems. The focus of this thesis is the development of Sensor Management algorithms which aim to optimise target tracking processes. Three principal theoretical and analytical contributions are presented which are related to the manner in which such problems are formulated and subsequently solved.Firstly, the trade-offs between optimising target tracking and other system-level objectives relating to expected operating lifetime are explored in an autonomous ground sensor scenario. This is achieved by modelling the observer trajectory control design as a probabilistic, information-theoretic, multiple-objective optimisation problem. This novel approach explores the relationships between the changes in sensor-target geometry that are induced by tracking performance measures and those relating to power consumption. This culminates in a novel observer trajectory control algorithm based onthe minimax approach.The second contribution is an analysis of the propagation of error through a limited-lookahead sensor control feedback loop. In the last decade, it has been shown that the use of such non-myopic (multiple-step) planning strategies can lead to superior performance in many Sensor Management scenarios. However, relatively little is known about the performance of strategies which use different horizon lengths. It is shown that, in the general case, planning performance is a function of the length of the horizon over which the optimisation is performed. While increasing the horizon maximises the chances of achieving global optimality, by revealing information about the substructureof the decision space, it also increases the impact of any prediction error, approximations, or unforeseen risk present within the scenario. These competing mechanisms aredemonstrated using an example tracking problem. This provides the motivation for a novel sensor control methodology that employs an adaptive length optimisation horizon. A route to selecting the optimal horizon size is proposed, based on a new non-myopic risk equilibrium which identifies the point where the two competing mechanisms are balanced.The third area of contribution concerns the development of a number of novel optimisation algorithms aimed at solving the resulting sequential decision making problems. These problems are typically solved using stochastic search methods such as Genetic Algorithms or Simulated Annealing. The techniques presented in this thesis are extensions of the recently proposed Repeated Weighted Boosting Search algorithm. In its originalform, it is only applicable to continuous, single-objective, ptimisation problems. The extensions facilitate application to mixed search spaces and Pareto multiple-objective problems. The resulting algorithms have performance comparable with Genetic Algorithm variants, and offer a number of advantages such as ease of implementation and limited tuning requirements

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

Cooperative Perception for Social Driving in Connected Vehicle Traffic

The development of autonomous vehicle technology has moved to the center of automotive research in recent decades. In the foreseeable future, road vehicles at all levels of automation and connectivity will be required to operate safely in a hybrid traffic where human operated vehicles (HOVs) and fully and semi-autonomous vehicles (AVs) coexist. Having an accurate and reliable perception of the road is an important requirement for achieving this objective. This dissertation addresses some of the associated challenges via developing a human-like social driver model and devising a decentralized cooperative perception framework. A human-like driver model can aid the development of AVs by building an understanding of interactions among human drivers and AVs in a hybrid traffic, therefore facilitating an efficient and safe integration. The presented social driver model categorizes and defines the driver\u27s psychological decision factors in mathematical representations (target force, object force, and lane force). A model predictive control (MPC) is then employed for the motion planning by evaluating the prevailing social forces and considering the kinematics of the controlled vehicle as well as other operating constraints to ensure a safe maneuver in a way that mimics the predictive nature of the human driver\u27s decision making process. A hierarchical model predictive control structure is also proposed, where an additional upper level controller aggregates the social forces over a longer prediction horizon upon the availability of an extended perception of the upcoming traffic via vehicular networking. Based on the prediction of the upper level controller, a sequence of reference lanes is passed to a lower level controller to track while avoiding local obstacles. This hierarchical scheme helps reduce unnecessary lane changes resulting in smoother maneuvers. The dynamic vehicular communication environment requires a robust framework that must consistently evaluate and exploit the set of communicated information for the purpose of improving the perception of a participating vehicle beyond the limitations. This dissertation presents a decentralized cooperative perception framework that considers uncertainties in traffic measurements and allows scalability (for various settings of traffic density, participation rate, etc.). The framework utilizes a Bhattacharyya distance filter (BDF) for data association and a fast covariance intersection fusion scheme (FCI) for the data fusion processes. The conservatism of the covariance intersection fusion scheme is investigated in comparison to the traditional Kalman filter (KF), and two different fusion architectures: sensor-to-sensor and sensor-to-system track fusion are evaluated. The performance of the overall proposed framework is demonstrated via Monte Carlo simulations with a set of empirical communications models and traffic microsimulations where each connected vehicle asynchronously broadcasts its local perception consisting of estimates of the motion states of self and neighboring vehicles along with the corresponding uncertainty measures of the estimates. The evaluated framework includes a vehicle-to-vehicle (V2V) communication model that considers intermittent communications as well as a model that takes into account dynamic changes in an individual vehicle’s sensors’ FoV in accordance with the prevailing traffic conditions. The results show the presence of optimality in participation rate, where increasing participation rate beyond a certain level adversely affects the delay in packet delivery and the computational complexity in data association and fusion processes increase without a significant improvement in the achieved accuracy via the cooperative perception. In a highly dense traffic environment, the vehicular network can often be congested leading to limited bandwidth availability at high participation rates of the connected vehicles in the cooperative perception scheme. To alleviate the bandwidth utilization issues, an information-value discriminating networking scheme is proposed, where each sender broadcasts selectively chosen perception data based on the novelty-value of information. The potential benefits of these approaches include, but are not limited to, the reduction of bandwidth bottle-necking and the minimization of the computational cost of data association and fusion post processing of the shared perception data at receiving nodes. It is argued that the proposed information-value discriminating communication scheme can alleviate these adverse effects without sacrificing the fidelity of the perception

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

Visualization Techniques in Space and Atmospheric Sciences

Unprecedented volumes of data will be generated by research programs that investigate the Earth as a system and the origin of the universe, which will in turn require analysis and interpretation that will lead to meaningful scientific insight. Providing a widely distributed research community with the ability to access, manipulate, analyze, and visualize these complex, multidimensional data sets depends on a wide range of computer science and technology topics. Data storage and compression, data base management, computational methods and algorithms, artificial intelligence, telecommunications, and high-resolution display are just a few of the topics addressed. A unifying theme throughout the papers with regards to advanced data handling and visualization is the need for interactivity, speed, user-friendliness, and extensibility

Autonomous Vehicle Good Citizenry Standard

69A3551747119The supplemental files to this record include an "Autonomous Vehicle Literature Review," and an Executive Summary.To establish a framework for considering these wraparound AV impacts in New York City, the NYU Rudin Center for Transportation Policy and Management led a multi-stakeholder initiative in conjunction with NYU\u2019s C2SMART, USDOT University Transportation Center. The team hosted three workshops in December 2021 addressing issues and opportunities in seven topic areas: Local Governance, Community Outreach, Integration with Public Transit, Equity, Accessibility, Safety and Data Privacy. Participants represented the public and private sectors, advocacy and civic organizations, and academia. In addition, the research team conducted a quantitative study of equitable deployment of new mobility in New York City. The study finds that the ideal data aggregation level depends on the reliability of the public data collected for that community, and recommends a new tool to help design the appropriate data structure for data sharing. Based on this foundational research and stakeholder input, seven mobility policy principles are presented as a preliminary framework for approaching the wraparound policies concerning the introduction of autonomous vehicles to New York City: safety, serve all NYers equitably, engage NY's diverse communities, coexist with public transportation, intelligent interactions with the city, New York City\u2019s ongoing sustainability efforts, and public-private partnerships