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Found 9 papers, 4 papers with code
Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving
With the rise of large foundation models, multimodal inputs offer the possibility of taking human language as a latent representation, thus enabling language-defined OOD detection.
Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models
A world model creates a surrogate world to train a controller and predict safety violations by learning the internal dynamic model of systems.
Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
Autonomous systems are increasingly implemented using end-to-end learning-based controllers.
Repairing Learning-Enabled Controllers While Preserving What Works
However, existing repair techniques do not preserve previously correct behaviors.
Curating Naturally Adversarial Datasets for Learning-Enabled Medical Cyber-Physical Systems
However, ensuring the robustness of these models is vital for building trustworthy AI systems.
Distributionally Robust Statistical Verification with Imprecise Neural Networks
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems.
How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
End-to-end learning has emerged as a major paradigm for developing autonomous systems.
Causal Repair of Learning-enabled Cyber-physical Systems
However, given the high diversity and complexity of LECs, it is challenging to encode domain knowledge (e. g., the CPS dynamics) in a scalable actual causality model that could generate useful repair suggestions.
Confidence Composition for Monitors of Verification Assumptions
To predict safety violations in a verified system, we propose a three-step confidence composition (CoCo) framework for monitoring verification assumptions.
