10 citations · 15 across the 12 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…
Deep Normalizing Flows for State Estimation
Harrison Delecki, Liam A. Kruse, Marc R. Schlichting +1
Safe and reliable state estimation techniques are a critical component of next-generation robotic systems. Agents in such systems must be able to reason about the intentions and tr…
Model-based Validation as Probabilistic Inference
Harrison Delecki, Anthony Corso, Mykel J. Kochenderfer
Estimating the distribution over failures is a key step in validating autonomous systems. Existing approaches focus on finding failures for a small range of initial conditions or m…
Risk-aware Meta-level Decision Making for Exploration Under Uncertainty
Joshua Ott, Sung-Kyun Kim, Amanda Bouman +6
Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, loc…
How Do We Fail? Stress Testing Perception in Autonomous Vehicles
Harrison Delecki, Masha Itkina, Bernard Lange +2
Autonomous vehicles (AVs) rely on environment perception and behavior prediction to reason about agents in their surroundings. These perception systems must be robust to adverse we…
FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget
Oriana Peltzer, Amanda Bouman, Sung-Kyun Kim +8
We present a method for autonomous exploration of large-scale unknown environments under mission time constraints. We start by proposing the Frontloaded Information Gain Orienteeri…