31 citations · 40 across the 5 of their papers we have counts for
5 papers
MPOGames: Efficient Multimodal Partially Observable Dynamic Games
Oswin So, Paul Drews, Thomas Balch +3
Game theoretic methods have become popular for planning and prediction in situations involving rich multi-agent interactions. However, these methods often assume the existence of a…
Vision-Based High Speed Driving with a Deep Dynamic Observer
Paul Drews, Grady Williams, Brian Goldfain +2
In this paper we present a framework for combining deep learning-based road detection, particle filters, and Model Predictive Control (MPC) to drive aggressively using only a monoc…
Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model
Paul Drews, Grady Williams, Brian Goldfain +2
We present a framework for vision-based model predictive control (MPC) for the task of aggressive, high-speed autonomous driving. Our approach uses deep convolutional neural networ…
Autonomous Racing with AutoRally Vehicles and Differential Games
Grady Williams, Brian Goldfain, Paul Drews +2
Safe autonomous vehicles must be able to predict and react to the drivers around them. Previous control methods rely heavily on pre-computation and are unable to react to dynamic e…
Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving
Grady Williams, Paul Drews, Brian Goldfain +2
We present an information theoretic approach to stochastic optimal control problems that can be used to derive general sampling based optimization schemes. This new mathematical me…