Incorporating Failure of Machine Learning in Dynamic Probabilistic Safety Assurance (2506.06868v1)

Abstract: Machine Learning (ML) models are increasingly integrated into safety-critical systems, such as autonomous vehicle platooning, to enable real-time decision-making. However, their inherent imperfection introduces a new class of failure: reasoning failures often triggered by distributional shifts between operational and training data. Traditional safety assessment methods, which rely on design artefacts or code, are ill-suited for ML components that learn behaviour from data. SafeML was recently proposed to dynamically detect such shifts and assign confidence levels to the reasoning of ML-based components. Building on this, we introduce a probabilistic safety assurance framework that integrates SafeML with Bayesian Networks (BNs) to model ML failures as part of a broader causal safety analysis. This allows for dynamic safety evaluation and system adaptation under uncertainty. We demonstrate the approach on an simulated automotive platooning system with traffic sign recognition. The findings highlight the potential broader benefits of explicitly modelling ML failures in safety assessment.

• The paper introduces a framework that integrates ML failure detection via SafeML with Bayesian Networks to enhance dynamic safety assurance in autonomous systems.
• The methodology monitors real-time distributional shifts using statistical distance measures to trigger adaptive safety mitigations.
• Experimental results in traffic sign recognition demonstrate robust fallback strategies that maintain system safety under variable conditions.

Overview of Probabilistic Safety Assurance Framework in Autonomous Vehicle Platooning

The paper "Incorporating Failure of Machine Learning in Dynamic Probabilistic Safety Assurance" presents a novel approach to integrating Machine Learning (ML) models and Bayesian Networks (BNs) to enhance safety assurance in autonomous vehicle platooning systems. With the advent of autonomous systems and their increasing dependency on ML for decision-making, addressing reasoning failures, particularly when facing distributional shifts, becomes crucial. The authors propose a framework that effectively incorporates failure detection mechanisms through SafeML, augmenting the classic safety paradigms with dynamic, probabilistic reasoning.

The paper leverages SafeML, a mechanism that detects distributional shifts and quantifies confidence in ML predictions by comparing operational data against training distributions using statistical distance measures. The integration with BNs allows for dynamic safety evaluation, enabling the system to adapt under uncertain and evolving conditions. The proposed framework is tested within a simulated environment focusing on vehicular platooning with traffic sign recognition, demonstrating how ML failures can be explicitly modeled and addressed in runtime safety assurance evaluations.

Key Findings and Contributions

• SafeML Integration: SafeML provides runtime safety assurance by monitoring distributional shifts in real-time. By incorporating statistical assessment of prediction reliability, it flags unreliable input samples, triggering safety-preserving mitigations in autonomous systems.
• Bayesian Network Framework: The use of BNs complements ML models by providing robust probabilistic reasoning under uncertainty. By modeling causal relationships among various dependability variables, BNs can dynamically adjust safety evaluations and mitigation strategies.
• Probabilistic Safety Metrics: The framework dynamically categorizes system states based on potential ML reasoning failures and general safety conditions, offering a nuanced risk evaluation scheme designed to manage uncertainty more effectively.

Numerical Results

In scenarios involving ML-based traffic sign recognition, SafeML successfully identified out-of-distribution instances that were misclassified by the CNN, highlighting significant statistical deviations. Even when contextual indications did not flag any risk, SafeML's rigorous statistical validation led to adaptive safety responses through the BN. The framework provided robust fallback strategies during such failures, ensuring safety under dynamic operations.

Implications for Autonomous Systems

The novel approach presented holds substantial practical implications. By explicitly modeling ML uncertainties and reasoning failures as part of safety analysis, the framework enhances adaptability and robustness in autonomous vehicle systems. This integration provides a blueprint for managing ML-derived unpredictabilities in real-time decision-making environments, ensuring safety is never compromised.

Speculations on Future Developments

Future AI systems should consider integrating statistical validation mechanisms akin to SafeML across various decision-making components. The paper's methodology can be extended to broader domains involving intelligent systems that thrive in unpredictable environments. Enhanced versions may incorporate multi-agent settings, temporal reasoning, and adaptive confidence thresholds to further refine safety assurance protocols.

The proposed framework serves as a step towards probabilistically aware machine learning deployments in safety-critical systems, offering a compelling approach to embedding comprehensive safety assurance even under machine-induced uncertainties.