6 papers
Importance Sampling and PCA for Finding Failures in Commercial Autonomous Vehicles
Hailey Warner, Duncan Eddy, Shreya Parjan +6
Methods for discovering rare failures in autonomous systems have so far been demonstrated almost exclusively in simulations with simple, academic driving stacks, leaving open wheth…
Diffusion Models for Safety Validation of Autonomous Driving Systems
Juanran Wang, Marc R. Schlichting, Harrison Delecki +1
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential fail…
Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems
Harrison Delecki, Marc R. Schlichting, Mansur Arief +3
Validating safety-critical autonomous systems in high-dimensional domains such as robotics presents a significant challenge. Existing black-box approaches based on Markov chain Mon…
Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
Liam A. Kruse, Alexandros E. Tzikas, Harrison Delecki +2
Importance sampling is a rare event simulation technique used in Monte Carlo simulations to bias the sampling distribution towards the rare event of interest. By assigning appropri…
An Addendum to NeBula: Towards Extending TEAM CoSTAR's Solution to Larger Scale Environments
Ali Agha, Kyohei Otsu, Benjamin Morrell +86
This paper presents an appendix to the original NeBula autonomy solution developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), participating in the DARPA Sub…
Failure Probability Estimation for Black-Box Autonomous Systems using State-Dependent Importance Sampling Proposals
Harrison Delecki, Sydney M. Katz, Mykel J. Kochenderfer
Estimating the probability of failure is a critical step in developing safety-critical autonomous systems. Direct estimation methods such as Monte Carlo sampling are often impracti…