Research on Safety Management Challenges and Strategies for End-to-End Autonomous Driving Systems

Abstract

With the transformation of artificial intelligence from a “rule-driven” to a “data-driven” paradigm, the end-to-end architecture has become a frontier direction in the development of intelligent driving systems. However, the “black-box” nature of its decision-making poses significant challenges to the current admission management framework based on modular design, resulting in insufficient regulatory foundations and incomplete policy coverage. To address these risks, a scenario-based dynamic risk quantification model is developed to provide a scientific basis for accurate assessment and hierarchical supervision. A full-chain safety management mechanism covering four dimensions is further established to systematically control risks throughout the entire lifecycle of the technology. Finally, three core policy optimization measures are formulated to facilitate the transition from “static access” to “dynamic access + continuous safety assessment”, to strengthen the supporting standards and technical systems, to define the mechanisms for hierarchical accountability and social risk sharing, and to establish a governance framework for the safe and orderly deployment of end-to-end intelligent driving systems.