AD Safety

Abstract: Yoav Hollander is a world-class expert in chip verification. The company he founded, Foretellix, brought coverage-driven verification (CDV) into autonomous driving. Recently he wrote a post pushing the methodology into a much larger arena: AI alignment. This post reads that cross-domain migration from my own research field — the SOTIF four-quadrant model, the tree-like structure of Robot SOTIF, and the standards-driven Chinese context. The core question stays the same throughout: how do you know what you don’t know? ...

Abstract: For intelligent driving to “drive like a human”, the prerequisite is understanding how humans actually drive. Starting from the concept of “human-like driving”, this post discusses five application directions for human driving behavior research, four paths to obtaining the answer, and the open-data practice our team is pursuing. First published on my WeChat channel in late March 2026; added to this blog in June 2026. Humans have always been fascinated by the study of their own behavior. ...

Abstract: ASIL E is not a published standard. Its real value is not the name of a higher integrity level, but the question it forces Level 4 and Level 5 autonomous-driving safety arguments to answer: when there is no human fallback, can the safety case still credit a human controller? For me, the useful translation is not “ASIL E compliance.” It is a no-human-fallback review lens, four evidence fields in ADSafetyPilot, and a feedback loop connecting ROAM, DRIVEResearch, and a field-monitoring-backed safety case. ...

Abstract: On June 2, 2026, the Chinese national standard project 机器人预期功能安全实施指南 entered public notice, with the comment period scheduled to close on July 2, 2026. I have put this direction into OpenTopic as the second open research theme: Robot SOTIF. The goal is not to copy autonomous-driving SOTIF directly into robotics, but to build an evidence chain from standards, ODD, scenarios, triggering conditions, physical interaction, LLM/VLA decision safety, and finally to a defensible safety case. ...

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: When the AI in a driverless car can’t handle the situation, does the industry have a coherent emergency-management playbook? It doesn’t — and worse, no reference standard exists. Starting from the March 31, 2026 Wuhan Apollo Go incident, this post walks through three recent body blows to the Robotaxi industry, scans every gap in the ISO / SAE / IEC / China standards landscape on remote operations, traces the fundamental regulatory pivot after Wuhan, and introduces ROAM (Remote Operations & Anomaly Management) — an open-source reference architecture with four modules, ten future operating models, and a 52-standard scan that confirms the void. ...

Abstract: In Q1 2026, “Harness Engineering” surfaced almost simultaneously at OpenAI, Anthropic, and the Chinese startup Nextie, and the “12 Primitives” converged in the open-source community as a shared taxonomy. This essay argues that essentially all mainstream investment in Harness has concentrated in a single dimension — user experience, performance, efficiency — while the dimension that actually determines market access in Safety-Critical domains (autonomous driving, medical AI, financial risk control) has been collectively skipped: safety compliance. By constructing a two-way mapping between the 12 Harness Primitives and SOTIF (ISO 21448), this essay identifies 12 concrete research directions, offered as a starting point for standardization bodies, corporate R&D, third-party institutions, and academic labs to jointly fill in this commons. A ~3000-word Chinese short form is available on the author’s WeChat channel. ...

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. ...