Quantitative Predictive Monitoring and Control for Safe Human-Machine Interaction
DOI: 10.48550/arxiv.2412.13365
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Summary
This paper addresses the safety challenges in human-machine interaction systems, specifically focusing on Type 1 Diabetes (T1D) management and semi-autonomous driving. The authors argue that ensuring safety requires holistic reasoning about AI systems and human operators while accounting for the inherent uncertainty of human behavior. To address this, they propose a quantitative predictive monitoring and control approach that predicts future states, monitors safety requirements under uncertainty, and adapts control actions accordingly. The methodology centers on Bayesian Recurrent Neural Networks (RNNs) to generate sequential predictions with uncertainty estimates, represented as flowpipe signals. The authors introduce Signal Temporal Logic with Uncertainty (STL-U) to formally specify safety requirements. A key contribution is a new quantitative monitor that computes a robustness degree interval, indicating the extent to which predicted uncertain traces satisfy or violate STL-U requirements. This quantitative feedback is used to define a novel loss function for calibrating the uncertainty estimation of Bayesian RNNs during training, guiding the selection of stochastic regularization techniques and dropout rates. Additionally, an adaptive controller adjusts actions based on these robustness degrees. Experiments were conducted using the FDA-approved UVA/PADOVA T1D patient simulator across adult, adolescent, and child virtual populations, as well as the CARLA simulator for autonomous driving. For uncertainty calibration, the proposed loss function outperformed baseline metrics in F1 scores for requirement satisfaction, effectively selecting optimal model configurations that avoided overestimating uncertainty. In predictive monitoring, the STL-U quantitative monitor provided significantly earlier hazard detection (average pre-alert time of ~23 minutes) compared to a baseline monitor that ignored uncertainty (~1–10 minutes). In closed-loop T1D management simulations, the adaptive controller reduced the average number of hypoglycemic and hyperglycemic hazards compared to a standard Basal-Bolus controller, particularly for adults. The study demonstrates that incorporating quantitative predictive monitoring into the control loop improves both the calibration of uncertainty estimates and the safety of automated systems. By providing fine-grained robustness metrics, the approach enables proactive adaptation of control actions, such as adjusting insulin dosages based on predicted risk levels. This framework offers a generalizable solution for enhancing safety in AI-driven systems where human uncertainty is a critical factor.
Key finding
The proposed quantitative predictive monitoring and control approach improves safety and effectiveness by reducing the number of hazards and enabling earlier detection of impending safety violations in simulated Type 1 Diabetes management and semi-autonomous driving scenarios.
Methodology
simulation_modeling
Sample size: 30
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: computational model