Intelligent driving safety assessment is undergoing a paradigm shift from "laboratory ideal" to "global stress test". Tesla's "ghost brake" incident exposed the misjudgment of the pure visual scheme on special light and shadow conditions, and the Uber autopilot death case revealed the fatal flaw of the decision-making algorithm in responding to sudden scenes.

These cases point to the triple vulnerabilities of the current security system: insufficient sensor redundancy, low coverage of edge cases, and ambiguity of human-machine rights and responsibilities.

Technology iteration is building a tighter security closed loop: the multi-modal sensor fusion architecture significantly improves the robustness of environmental perception, such as Mobileye's radar + lidar + camera solution to reduce the obstacle missed detection rate by 82%;

強化學習演算法通過500萬小時虛擬模擬測試，可預演極端場景下的決策邏輯；車路協同技術使智慧路側單元實時補償車載系統盲區，形成雙重安全冗餘。

Regulatory formulation urgently requires the establishment of a "technical-ethical-legal" three-in-one regulatory framework. The European Union's General Safety Regulation requires autonomous driving systems to pass "explainability tests", China's Road Test Regulations for Intelligent Connected Vehicles (ICVs) specify tiered access criteria, and the United States reconstructs the insurance claims mechanism through the Collision Liability Act.

In the future, regulations need to focus on three major areas: establishing a dynamic scenario library certification system, formulating data security lifecycle management standards, building a cross-brand accident data sharing mechanism, and installing a "rule of law bumper" for intelligent driving.