SOTIF

Abstract: End-to-end architectures are moving from research papers to mass production, yet the cornerstone of global autonomous driving safety evaluation — scenario-based development and testing — still rests on the assumption that systems can be decomposed into perception, planning, and control modules. This article systematically analyzes five structural challenges that scenario methods face in the end-to-end era, argues that they remain valid but are no longer sufficient, and proposes an evolutionary path that supplements scenario methods with large-scale aerial naturalistic driving data within a three-layer collaborative framework. ...

Abstract: In March 2026, JAMA released the fourth edition (Ver.4.0) of the SAKURA Automated Driving Safety Evaluation Framework — a 344-page national-level safety evaluation technical document. This article systematically examines this safety evaluation system jointly developed by Toyota, Honda, Nissan, and other major Japanese automakers, covering its corporate value, engineering perspectives, core methodology, and the frontier challenges posed by the end-to-end AI era, while exploring its implications for China’s standardization efforts. ...

Abstract: In March 2026, Germany’s VDA published the global automotive industry’s first standardized guideline for AI quality management — VDA 20 AI in Quality Management (191 pages). This article provides an in-depth analysis of its AIQM three-tier risk classification, 80-item checklist, and 12 application cases. It examines the reference value for China’s autonomous driving industry and explores China’s leading advantages and window of opportunity in end-to-end evaluation methodologies and data infrastructure. ...