Abstract:

The current intelligent driving systems constructed based on AI computing are constrained by the inexplicability of AI computation and the infinite dynamics of traffic scenarios. Theoretically， they struggle to form a logically self-consistent safety system， while frequent real-world accidents have eroded industry credibility. In this paper， an innovative solution is proposed from the perspectives of information theory and systems theory： introducing a real-time verification mechanism based on comparative eigen-information and designing an industrial implementation path. Breaking through traditional paradigms， the study defines safe states and constructs an information model for traffic scenarios to demonstrate the core principle of the real-time verification mechanism. By comparing the eigen-information of the physical world and the digital world， AI calculation errors are detected in real time to ensure the system theoretically converges to a safe state during operation. Through rigorous theoretical deduction， the mechanism's principles exhibit universality， applying not only to reconstructing intelligent driving systems but also providing a new explanatory dimension for the safety principles of electronic and communication systems.

Key words: scenario dimension information, scene intrinsic information, conservation and symmetry, real-time verification mechanism, time and space occupation