ISO 21448

Abstract: L2 driver assistance, L3/L4 autonomous driving, and humanoid and passenger-carrying quadruped robots share one cybersecurity methodology yet differ in who is responsible, how they are regulated, and how deeply cybersecurity couples with safety. This post systematically maps the requirements, differences, standards/regulations, and best practices across the three families, with a focus on how deeply cybersecurity integrates with functional safety (FuSa), SOTIF, and whole-machine safety. Key takeaways All three families have entered the “cybersecurity is an entry gate” stage, but with a clear gradient: L2 is enforced, L3/L4 has the highest bar, and mobile physical AI is weakest yet fastest-moving. The essence: L2 treats the network as an information asset, L3/L4 as a safety system, and physical AI as a physical safety device. Integration: L2 is coordinated, L3/L4 is integrated/converging, and mobile physical AI is mechanistically unified but method-empty. The adversarial AI surface (data poisoning, evasion, model theft) is the common new gap in 2026, under-covered by 21434/62443. Best practices are highly shared (TARA, defense in depth, SBOM, secure OTA, monitoring, pentest, PIA) and transferable across bodies. 1. Framework: three threads + two axes Three threads decide requirements: whether a human bails out, what physical environment the system operates in, and what the regulator can grab. L2 has a human fallback, L3/L4 has none, and mobile physical AI controls the physical body and balance — deciding whether cybersecurity is an “information asset problem,” a “safety system problem,” or a “physical safety device problem.” ...

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: 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: 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: 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. ...