186 citations · 440 across the 14 of their papers we have counts for
6 papers · 1 filter
Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks
John Mern, Kyle Julian, Rachael E. Tompa +1
A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft. Existing sense-and-avoid methods often require specialized sensors that ar…
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning
Kyle D. Julian, Mykel J. Kochenderfer
Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircra…
Deep Neural Network Compression for Aircraft Collision Avoidance Systems
Kyle D. Julian, Mykel J. Kochenderfer, Michael P. Owen
One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic pr…
Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction
Kyle D. Julian, Mykel J. Kochenderfer
Small unmanned aircraft can help firefighters combat wildfires by providing real-time surveillance of the growing fires. However, guiding the aircraft autonomously given only wildf…
Toward Scalable Verification for Safety-Critical Deep Networks
Lindsey Kuper, Guy Katz, Justin Gottschlich +3
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Form…
Decomposition Methods with Deep Corrections for Reinforcement Learning
Maxime Bouton, Kyle Julian, Alireza Nakhaei +2
Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility…