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Found 10 papers, 4 papers with code
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
Hence, it is important that these prediction models are extensively tested in various test scenarios involving interactive behaviors prior to deployment.
Addressing the IEEE AV Test Challenge with Scenic and VerifAI
Sampling from an abstract scenario yields many different concrete scenarios which can be run as test cases for the AV.
Parallel and Multi-Objective Falsification with Scenic and VerifAI
Falsification has emerged as an important tool for simulation-based verification of autonomous systems.
Scenic: A Language for Scenario Specification and Data Generation
We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time.
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI
We demonstrate a unified approach to rigorous design of safety-critical autonomous systems using the VerifAI toolkit for formal analysis of AI-based systems.
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World
We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world.
VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
Constrained Sampling and Counting: Universal Hashing Meets SAT Solving
Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification.
Distribution-Aware Sampling and Weighted Model Counting for SAT
We present a novel approach that works with a black-box oracle for weights of assignments and requires only an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and sampling problems.
