31 citations · 49 across the 5 of their papers we have counts for
6 papers
Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics
Grady Williams, Brian Goldfain, James M. Rehg +1
We consider the problem of online adaptation of a neural network designed to represent vehicle dynamics. The neural network model is intended to be used by an MPC control law to au…
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…
AutoRally An open platform for aggressive autonomous driving
Brian Goldfain, Paul Drews, Changxi You +4
This article presents AutoRally, a 15 scale robotics testbed for autonomous vehicle research. AutoRally is designed for robustness, ease of use, and reproducibility, so that a t…
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…