Future Vision of Dynamic Certification Schemes for Autonomous Systems

• URL: http://arxiv.org/abs/2308.10340v1
• Date: Sun, 20 Aug 2023 19:06:57 GMT
• Title: Future Vision of Dynamic Certification Schemes for Autonomous Systems
• Authors: Dasa Kusnirakova and Barbora Buhnova
• Abstract summary: We identify several issues with the current certification strategies that could pose serious safety risks. We highlight the inadequate reflection of software changes in constantly evolving systems and the lack of support for systems' cooperation. Other shortcomings include the narrow focus of awarded certification, neglecting aspects such as the ethical behavior of autonomous software systems.
• Score: 3.151005833357807
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As software becomes increasingly pervasive in critical domains like autonomous driving, new challenges arise, necessitating rethinking of system engineering approaches. The gradual takeover of all critical driving functions by autonomous driving adds to the complexity of certifying these systems. Namely, certification procedures do not fully keep pace with the dynamism and unpredictability of future autonomous systems, and they may not fully guarantee compliance with the requirements imposed on these systems. In this paper, we have identified several issues with the current certification strategies that could pose serious safety risks. As an example, we highlight the inadequate reflection of software changes in constantly evolving systems and the lack of support for systems' cooperation necessary for managing coordinated movements. Other shortcomings include the narrow focus of awarded certification, neglecting aspects such as the ethical behavior of autonomous software systems. The contribution of this paper is threefold. First, we analyze the existing international standards used in certification processes in relation to the requirements derived from dynamic software ecosystems and autonomous systems themselves, and identify their shortcomings. Second, we outline six suggestions for rethinking certification to foster comprehensive solutions to the identified problems. Third, a conceptual Multi-Layer Trust Governance Framework is introduced to establish a robust governance structure for autonomous ecosystems and associated processes, including envisioned future certification schemes. The framework comprises three layers, which together support safe and ethical operation of autonomous systems.

Related papers

• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• Open Challenges in the Formal Verification of Autonomous Driving [0.0]
  We present a real-world case study of an autonomous driving system. We identify key open challenges associated with its development and integration. We explore how formal verification techniques can address these challenges to ensure system reliability and safety.
  arXiv  Detail & Related papers  (2024-11-21T18:09:35Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• A Roadmap for Simulation-Based Testing of Autonomous Cyber-Physical Systems: Challenges and Future Direction [5.742965094549775]
  This paper pioneers a strategic roadmap for simulation-based testing of autonomous systems. Our paper discusses the relevant challenges and obstacles of ACPSs, focusing on test automation and quality assurance.
  arXiv  Detail & Related papers  (2024-05-02T07:42:33Z)
• Security Challenges in Autonomous Systems Design [1.864621482724548]
  With the independence from human control, cybersecurity of such systems becomes even more critical. With the independence from human control, cybersecurity of such systems becomes even more critical. This paper thoroughly discusses the state of the art, identifies emerging security challenges and proposes research directions.
  arXiv  Detail & Related papers  (2023-11-05T09:17:39Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)
• Rethinking Certification for Higher Trust and Ethical Safeguarding of Autonomous Systems [6.24907186790431]
  We discuss the motivation for the need to modify the current certification processes for autonomous driving systems. We identify a number of issues with the proposed certification strategies, which may impact the systems substantially.
  arXiv  Detail & Related papers  (2023-03-16T15:19:25Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Systems Challenges for Trustworthy Embodied Systems [0.0]
  A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. It is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in
  arXiv  Detail & Related papers  (2022-01-10T15:52:17Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.