14 citations · 15 across the 12 of their papers we have counts for
12 papers
Protection Levels for Vision-Based Pose Estimation
Olivia Beyer Bruvik, Romeo Valentin, Marc R. Schlichting +2
Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presente…
Real-Time Flight Test Maneuver Selection with Monte Carlo Tree Search
Nicholas E. Bostock, Helen Pruitt-Kennett, Marc R. Schlichting +1
Flight test is shifting toward a data-centric approach in which data contribute to model refinement, reducing reliance on pre-scripted test points. An open problem is how to sequen…
How Language Models Fail: Token-Level Signatures of Committed and Persistent Reasoning Failures
Tanvi Thoria, Kiana Jafari, Marc R. Schlichting +1
Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize these failures using token-level u…
Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System
Romeo Valentin, Olivia Beyer Bruvik, Marc R. Schlichting +1
EASA's learning-assurance guidance requires data-driven aviation systems to build and monitor their own situation representation, yet for neural networks the technical means to pro…
Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplers
Juanran Wang, Marc R. Schlichting, Mykel J. Kochenderfer
High-risk traffic zones such as intersections are a major cause of collisions. This study leverages deep generative models to enhance the safety of autonomous vehicles in an inters…
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…
Scalable Importance Sampling in High Dimensions with Low-Rank Mixture Proposals
Liam A. Kruse, Marc R. Schlichting, Mykel J. Kochenderfer
Importance sampling is a Monte Carlo technique for efficiently estimating the likelihood of rare events by biasing the sampling distribution towards the rare event of interest. By…
LeRAAT: LLM-Enabled Real-Time Aviation Advisory Tool
Marc R. Schlichting, Vale Rasmussen, Heba Alazzeh +5
In aviation emergencies, high-stakes decisions must be made in an instant. Pilots rely on quick access to precise, context-specific information -- an area where emerging tools like…
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…
SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning
Marc R. Schlichting, Nina V. Boord, Anthony L. Corso +1
Discovering potential failures of an autonomous system is important prior to deployment. Falsification-based methods are often used to assess the safety of such systems, but the co…
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…
Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning
Marc R. Schlichting, Stefan Notter, Walter Fichter
Reinforcement learning-based path planning for multi-agent systems of varying size constitutes a research topic with increasing significance as progress in domains such as urban ai…