End-to-End

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: 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. ...