14 citations · 15 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…