Title: A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0 Show Chinese title

Title: 基于 Assurance 2.0 的自动驾驶车辆安全保证可扩展框架

Abstract: Assurance 2.0 is a modern framework developed to address the assurance challenges of increasingly complex, adaptive, and autonomous systems. Building on the traditional Claims-Argument-Evidence (CAE) model, it introduces reusable assurance theories and explicit counterarguments (defeaters) to enhance rigor, transparency, and adaptability. It supports continuous, incremental assurance, enabling innovation without compromising safety. However, limitations persist in confidence measurement, residual doubt management, automation support, and the practical handling of defeaters and confirmation bias. This paper presents \textcolor{black}{a set of decomposition frameworks to identify a complete set of safety arguments and measure their corresponding evidence.} Grounded in the Assurance 2.0 paradigm, the framework is instantiated through a structured template and employs a three-tiered decomposition strategy. \textcolor{black}{A case study regarding the application of the decomposition framework in the end-to-end (E2E) AI-based Self-Driving Vehicle (SDV) development is also presented in this paper.} At the top level, the SDV development is divided into three critical phases: Requirements Engineering (RE), Verification and Validation (VnV), and Post-Deployment (PD). Each phase is further decomposed according to its Product Development Lifecycle (PDLC). To ensure comprehensive coverage, each PDLC is analyzed using an adapted 5M1E model (Man, Machine, Method, Material, Measurement, and Environment). Originally developed for manufacturing quality control, the 5M1E model is reinterpreted and contextually mapped to the assurance domain. This enables a multi-dimensional decomposition that supports fine-grained traceability of safety claims, evidence, and potential defeaters. Show Chinese abstract

Abstract: 保证2.0是一种现代框架，旨在解决日益复杂、自适应和自主系统保证挑战。 在传统的主张-论据-证据（CAE）模型基础上，它引入了可重用的保证理论和明确的反论点（反驳者），以增强严谨性、透明度和适应性。 它支持持续的、渐进的保证，使创新成为可能而不影响安全性。 然而，在信心测量、剩余怀疑管理、自动化支持以及反驳者和确认偏见的实际处理方面仍存在局限。 本文提出 \textcolor{black}{一组分解框架，用于识别安全论证的完整集合并测量其相应的证据。} 基于保证2.0范式，该框架通过结构化模板实现，并采用三层分解策略。 \textcolor{black}{关于在基于端到端（E2E）人工智能的自动驾驶车辆（SDV）开发中应用分解框架的一个案例研究也在本文中进行了介绍。} 在顶层，SDV开发分为三个关键阶段：需求工程（RE）、验证与确认（VnV）和部署后（PD）。 每个阶段根据其产品开发生命周期（PDLC）进一步分解。 为确保全面覆盖，每个PDLC使用经过调整的5M1E模型（人员、机器、方法、材料、测量和环境）进行分析。 该5M1E模型最初用于制造质量控制，被重新解释并情境化映射到保证领域。 这使得多维分解成为可能，支持对安全主张、证据和潜在反驳者的细粒度可追溯性。