Cover Image

How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mao, Zhenjiang, Sobolewski, Carson, Ruchkin, Ivan
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
by: Mao, Zhenjiang, et al.
Published: (2025)

Generalizable Image Repair for Robust Visual Control
by: Sobolewski, Carson, et al.
Published: (2025)

Zero-shot Safety Prediction for Autonomous Robots with Foundation World Models
by: Mao, Zhenjiang, et al.
Published: (2024)

Physically Interpretable World Models via Weakly Supervised Representation Learning
by: Mao, Zhenjiang, et al.
Published: (2024)

Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic
by: Mao, Zhenjiang, et al.
Published: (2025)

Four Principles for Physically Interpretable World Models
by: Peper, Jordan, et al.
Published: (2025)

Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning
by: Mao, Zhenjiang, et al.
Published: (2026)

Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving
by: Mao, Zhenjiang, et al.
Published: (2024)

Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
by: Mao, Zhenjiang, et al.
Published: (2026)

I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
by: Liu, Yuhang, et al.
Published: (2025)

How Many Tools Should an LLM Agent See? A Chance-Corrected Answer
by: Repantis, Vyzantinos, et al.
Published: (2026)

Bridging Dimensions: Confident Reachability for High-Dimensional Controllers
by: Geng, Yuang, et al.
Published: (2023)

I See, Therefore I Do: Estimating Causal Effects for Image Treatments
by: Thorat, Abhinav, et al.
Published: (2024)

Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Models
by: Mao, Zhenjiang, et al.
Published: (2026)

I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks
by: Oliynyk, Daryna, et al.
Published: (2025)

Finite-Sample Analysis of Elimination in Active Hypothesis Testing
by: Lin, Ziyuan, et al.
Published: (2026)

Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
by: Park, Young-Jin, et al.
Published: (2024)

Loss-Controlling Calibration for Predictive Models
by: Wang, Di, et al.
Published: (2023)

Learning-Based Shielding for Safe Autonomy under Unknown Dynamics
by: Reed, Robert, et al.
Published: (2024)

Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction
by: Chen, Junyi, et al.
Published: (2024)

On Safety in Safe Bayesian Optimization
by: Fiedler, Christian, et al.
Published: (2024)

Learning Safe Autonomous Driving Policies Using Predictive Safety Representations
by: Keswani, Mahesh, et al.
Published: (2025)

Safe Transformer: An Explicit Safety Bit For Interpretable And Controllable Alignment
by: Feng, Jingyuan, et al.
Published: (2026)

The Open Autonomy Safety Case Framework
by: Wagner, Michael, et al.
Published: (2024)

Learning Contextual Runtime Monitors for Safe AI-Based Autonomy
by: Luque-Cerpa, Alejandro, et al.
Published: (2026)

Chance-Constrained Inference for Hallucination Risk Control in Large Language Models
by: Mohandas, Sreenivasan
Published: (2026)

SafeFix: Targeted Model Repair via Controlled Image Generation
by: Xu, Ouyang, et al.
Published: (2025)

Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
by: Jia, Zeyang, et al.
Published: (2023)

Conformal Predictive Programming for Chance Constrained Optimization
by: Zhao, Yiqi, et al.
Published: (2024)

What is in Your Safe Data? Identifying Benign Data that Breaks Safety
by: He, Luxi, et al.
Published: (2024)

Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity
by: Li, Victor, et al.
Published: (2025)

VoxSafeBench: Not Just What Is Said, but Who, How, and Where
by: Wang, Yuxiang, et al.
Published: (2026)

Safe Control using Learned Safety Filters and Adaptive Conformal Inference
by: Huriot, Sacha, et al.
Published: (2026)

The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy
by: Overman, William, et al.
Published: (2025)

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation
by: van der Hoorn, Diede P. M., et al.
Published: (2025)

Loss Given Default Prediction Under Measurement-Induced Mixture Distributions: An Information-Theoretic Approach
by: Marín, Javier
Published: (2025)

Driving Safety Prediction and Safe Route Mapping Using In-vehicle and Roadside Data
by: Huang, Yufei, et al.
Published: (2022)

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning
by: Bae, Jinsoo, et al.
Published: (2025)

Safety Pretraining: Toward the Next Generation of Safe AI
by: Maini, Pratyush, et al.
Published: (2025)

Online Slip Detection and Friction Coefficient Estimation for Autonomous Racing
by: Oeltjen, Christopher, et al.
Published: (2025)