As intelligent vehicle technologies evolve, reliable driver monitoring systems have become increasingly critical for ensuring the safety of human drivers and operational reliability. This paper proposes a novel visual computing framework for Driver Fatigue Monitoring Systems (DFMSs) based on hierarchical decoupling and scenario element analysis, specifically designed for intelligent transportation environments. By treating the monitoring system as an engineering-level embodied perception–decision system deployed within the vehicle, rather than a purely disembodied vision module, the framework decouples low-level algorithmic perception from application-layer decision logic, enabling a more granular evaluation of visual computing performance in real-world scenarios. We leverage Python 3.9-driven automated test case generation to simulate diverse environmental variables, improving testing efficiency by 50% over traditional manual methods. The system utilizes deep learning-based visual computing to achieve high-fidelity monitoring of eye closure (PERCLOS, EAR), yawning (MAR), and head pose dynamics, enabling real-time assessment of the driver’s state within the embodied system loop. Comparative benchmarking reveals that our framework significantly outperforms existing models in visual understanding accuracy, achieving perfect confidence scores (1.000) for eye closure and smoking behavior detection, while drastically reducing false positives in mobile phone usage detection (misidentification rate: 0.016 vs. 0.805). These results demonstrate that an embodied approach to visual perception enhances the robustness and reliability of driver monitoring systems deployed in real vehicles, providing a scalable pathway for the development of next-generation intelligent transportation safety standards.

Digital transformation has increased pressure on firms to redesign finance processes, yet evidence on the flexibility consequences of such redesign remains limited. This study examines whether financial process reengineering in the finance function, proxied by financial shared service center (FSSC) adoption, is associated with lower financial flexibility. Using a panel of Chinese A-share non-financial listed firms from 2013 to 2022, the analysis combines fixed-effects regressions with a staggered event-study difference-in-differences design based on first-adoption timing inferred from the Management Discussion and Analysis section of annual reports. The baseline estimates show a negative association between FSSC adoption and overall financial flexibility, and the reduction is more pronounced for cash flexibility than for debt flexibility. The timing-based evidence shows jointly insignificant lead coefficients within the plotted pre-adoption window and negative post-adoption dynamics, with the strongest declines emerging one to two years after first adoption. Additional mechanism evidence indicates that investment becomes less sensitive to internally generated cash after adoption, while indirect proxies show higher selling, general, and administrative (SG&A) intensity and lower external financing constraints. Overall, the findings are consistent with weaker internal liquidity redeployment after adoption. They suggest that firms should evaluate shared-service reforms not only by cost savings and process standardization, but also by their implications for liquidity responsiveness and internal capital allocation.