New massless and massive infinite derivative gravity in three dimensions

In this paper we will consider the most general quadratic curvature action with infinitely many covariant derivatives of massless gravity in three spacetime dimensions. The action is parity invariant and torsion-free and contains the same off-shell degrees of freedom as the Einstein-Hilbert action in general relativity. In the ultraviolet, with an appropriate choice of the propagator given by the exponential of an entire function, the point-like curvature singularity can be smoothened to a Gaussian distribution, while in the infrared the theory reduces to general relativity. We will also show how to embed new massive gravity in ghost-free infinite derivative gravity in Minkowski background as one of the infrared limits. Finally, we will provide the tree-level unitarity conditions for infinite derivative gravity in presence of a cosmological constant in deSitter and Anti-deSitter spacetimes in three dimensions by perturbing the geometries.Comment: 26 page

Trustworthiness assurance assessment for high-risk AI-based systems

This work proposes methodologies for ensuring the trustworthiness of high-risk artificial intelligence (AI) systems (AIS) to achieve compliance with the European Union’s (EU) AI Act. Highrisk classified AIS must fulfill seven requirements to be considered trustworthy and human-centric, and subsequently be considered for deployment. These requirements are equally important, mutually supportive, and should be implemented and evaluated throughout the AI lifecycle. The assurance of trustworthiness is influenced by ethical considerations, amongst others. Hence, the operational design domain (ODD) and behavior competency (BC) concepts from the automated driving domain are utilized in risk assessment strategies to quantify different types of residual risks. The methodology presented is guided by the consistent application of the ODD and its related BC concept throughout the entire AI lifecycle, focusing on the trustworthiness assurance framework and its associated process as the main pillars for AIS certification. The achievement of the overall objective of trustworthy and human-centric AIS is divided into seven interconnected sub-goals: the formulation of use restrictions, the trustworthiness assurance/argument itself, the identification of dysfunctional cases, the utilization of scenario This work proposes methodologies for ensuring the trustworthiness of high-risk artificial intelligence (AI) systems (AIS) to achieve compliance with the European Union’s (EU) AI Act. High-risk classified AIS must fulfill seven requirements to be considered trustworthy and human-centric, and subsequently be considered for deployment. These requirements are equally important, mutually supportive, and should be implemented and evaluated throughout the AI lifecycle. The assurance of trustworthiness is influenced by ethical considerations, amongst others. Hence, the operational design domain (ODD) and behavior competency (BC) concepts from the automated driving domain are utilized in risk assessment strategies to quantify different types of residual risks. The methodology presented is guided by the consistent application of the ODD and its related BC concept throughout the entire AI lifecycle, focusing on the trustworthiness assurance framework and its associated process as the main pillars for AIS certification. The achievement of the overall objective of trustworthy and human-centric AIS is divided into seven interconnected sub-goals: the formulation of use restrictions, the trustworthiness assurance/argument itself, the identification of dysfunctional cases, the utilization of scenario databases and datasets, the application of metrics for evaluation, the implementation of the proposed concept across the AI lifecycle, and sufficient consideration of human factors. The role of standards in the assurance process is discussed, considering any existing gaps and areas for improvement. The work concludes with a summary of the developed approach, highlighting key takeaways and action points. Finally, a roadmap to ensure trustworthy and human-centric behavior of future AIS is outlined

Development and experimental validation of high performance embedded intelligence and fail-operational urban surround perception solutions of the PRYSTINE project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.</p

Twisted warped entanglement entropy

The aim of the thesis is to calculate the entanglement entropy of an interval in different two-dimensional warped conformal field theories. The result by Castro et al [key-5] is generalized to a second WCFT with a different symmetry algebra. This is done in two ways: First using the Rindler method and second using the replica trick. The new WCFT is particularly interesting because it appears as holographic dual of a boosted Rindler-spacetime. On the gravitational side, entanglement entropy is much easier to compute and I show that the results agree if one locates the field theory on the horizon at r=0 rather then at r towards infinity. This statement is shown to be also true for the slightly more involved case of boosted Rindler-AdS.4

Open string field theory with stubs

Abstract There are various reasons why adding stubs to the vertices of open string field theory (OSFT) is interesting: the stubs can not only tame certain singularities and make the theory more well-behaved, but also the new theory shares a lot of similarities with closed string field theory, which helps to improve our understanding of its structure and possible solutions. In this paper we explore two natural ways of implementing stubs into the framework of OSFT, resulting in an A ∞ -algebra giving rise to infinitely many vertices. We find two distinct consistent actions, both generated by a field redefinition, interestingly sharing the same equations of motion. In the last section we illustrate their relationship and physical meaning by applying our construction to nearly marginal solutions

Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

Virtual testing using simulation will play a significant role in future safety validation procedures for automated driving systems, as it provides the needed scalability for executing a scenario-based assessment approach. This article combines multiple essential aspects that are necessary for the virtual validation of such systems. First, a general framework that contains the vital subsystems needed for virtual validation is introduced. Secondly, the interfaces between the subsystems are explored. Additionally, the concept of model fidelities is presented and extended towards all relevant subsystems. For an automated lane-keeping system with two different definitions of an operational design domain, all relevant subsystems are defined and integrated into an overall simulation framework. The resulting difference between both operational design domains is the occurrence of lateral manoeuvres, leading to greater demands of the fidelity of the vehicle dynamics model. The simulation results support the initial assumption that by extending the operation domain, the requirements for all subsystems are subject to adaption. As an essential aspect of harmonising virtual validation frameworks, the article identifies four separate layers and their corresponding parameters. In particular, the tool-specific co-simulation capability layer is critical, as it enables model exchange through consistently defined interfaces and reduces the integration effort. The introduction of this layered architecture for virtual validation frameworks enables further cross-domain collaboration

Virtual Risk Assessment for the Deployment of Autonomous Shuttles

In recent years, trials of autonomous shuttle vehicles have been conducted worldwide. Currently, there exists no generalized process model for deployment and continuous operation of shuttles. Shuttle suppliers use their own developed procedures, making it difficult for the relevant stakeholders (e.g., public authorities) to assess the risk of potential shuttle deployment. The Digibus® Austria flagship project, among other goals, develops an approach for the virtual risk assessment of identified critical spots along proposed shuttle paths. Embedded into the deployment process, this serves as a significant body of evidence for safety assurance in shuttle deployment. Conducted simulation studies optimizing the shuttle’s trajectory for concrete maneuvers, along with derived requirements for the associated virtual environment, are part of the first noteworthy outcomes. Concretely, the developed virtual environment is integrated in the framework used for virtual validation. The framework is then used for a detailed evaluation of a right-turn maneuver, analyzing possible shuttle trajectories. Considerable differences in sensor coverage at the shuttle’s stopping point can be shown. Conclusively, by utilizing the shuttle’s restricted operational domain, the proposed virtual risk assessment is considered the first step toward a general procedure for the safety assurance of automated vehicles

Validating SuperHuman Automated Driving Performance

Closed-loop validation of autonomous vehicles is an open problem, significantly influencing development and adoption of this technology. The main contribution of this paper is a novel approach to reproducible, scenario-based validation that decouples the problem into several sub-problems, while avoiding to brake the crucial couplings. First, a realistic scenario is generated from the real urban traffic. Second, human participants, drive in a virtual scenario (in a driving simulator), based on the real traffic. Third, human and automated driving trajectories are reproduced and compared in the real vehicle on an empty track without traffic. Thus, benefits of automation with respect to safety, efficiency and comfort can be clearly benchmarked in a reproducible manner. Presented approach is used to benchmark performance of SBOMP planner in one scenario and validate SuperHuman driving performance.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Vehicle

Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project&mdash;PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck