Intelligent Driving

Abstract: OpenODC is an open-source project that turns the Chinese national standard GB/T 45312-2025 (Intelligent Connected Vehicles — Operational Design Conditions for Automated Driving Systems) into a machine-readable public dataset. It currently includes a 144-element ODC schema, six public sample profiles (Tesla FSD Supervised, Tesla China ADAS, Huawei Qiankun ADS 4, Apollo Go Wuhan operations, XPeng XNGP, Pony.ai Gen-7 Robotaxi), a coverage matrix, dual developer-/consumer-views, and a planned OEM-evidence workbench. This post is both the project’s origin note and a public invitation — including the explicit option to hand the project off to a more suitable steward, free of charge. ...

Abstract: In early 2026, Harness Engineering rose quickly in the AI engineering community, becoming a third-generation methodology after Prompt Engineering and Context Engineering. Starting from the core concept of Harness Engineering, this article systematically analyzes its deep correspondence with today’s end-to-end intelligent-driving systems across the full lifecycle. It argues that the two fields are structurally isomorphic in their control-theoretic framework, improvement loops, and philosophy of failure response. It also discusses the reference value of Harness Engineering for intelligent-driving user experience and safety engineering, especially SOTIF / ISO 21448. The central finding is that Harness Engineering and automotive safety engineering are not superficially similar metaphors. They are two independently evolved solutions to the same class of root problems, sharing the same underlying operating system. ...

Abstract: Bosch Lead Engineer Thejeswarareddy R open-sourced an agent system that systematically injects automotive engineering standards into Claude Code, covering 75+ skill categories. I forked it and added autonomous driving safety standards (ISO 21448/34502/4804, etc.), upcoming mandatory Chinese national standards, and in-depth SOTIF engineering practices. This article breaks down the project’s architectural highlights and my additions. Figure 1 If you are a junior functional safety engineer in the automotive industry, you have almost certainly lived through this scenario: ...

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. ...