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Found 6 papers, 0 papers with code
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts.
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety.
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
In this paper, we present a platform to model dynamic and interactive scenarios, generate the scenarios in simulation with different modalities of labeled sensor data, and collect this information for data augmentation.
Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning
We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning.
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
It is programmatic in that scenario representation is a program in a domain-specific probabilistic programming language which can be used to generate synthetic data to test a given perception module.
Generating Semantic Adversarial Examples with Differentiable Rendering
In this paper we explore semantic adversarial examples (SAEs) where an attacker creates perturbations in the semantic space representing the environment that produces input for the ML model.
