8 papers
Vision-Conditioned Variational Bayesian Last Layer Dynamics Models
Paul Brunzema, Thomas Lew, Ray Zhang +3
Agile control of robotic systems often requires anticipating how the environment affects system behavior. For example, a driver must perceive the road ahead to anticipate available…
Dynamic Association of Semantics and Parameter Estimates by Filtering
Marcus Greiff, Ray Zhang, Thomas Lew +1
We propose a probabilistic semantic filtering framework in which parameters of a dynamical system are inferred and associated with a closed set of semantic classes in a map. We ext…
Semantic Property Maps for Driving Applications
Marcus Greiff, Ray Zhang, Takeru Shirasawa +1
We consider the problem of estimating the parameters of a vehicle dynamics model for predictive control in driving applications. Instead of solely using the instantaneous parameter…
Differentiable Model Predictive Control on the GPU
Emre Adabag, Marcus Greiff, John Subosits +1
Differentiable model predictive control (MPC) offers a powerful framework for combining learning and control. However, its adoption has been limited by the inherently sequential na…
Cyber Racing Coach: A Haptic Shared Control Framework for Teaching Advanced Driving Skills
Congkai Shen, Siyuan Yu, Yifan Weng +7
This study introduces a haptic shared control framework designed to teach human drivers advanced driving skills. In this context, shared control refers to a driving mode where the…
Spatial Envelope MPC: High Performance Driving without a Reference
Siyuan Yu, Congkai Shen, Yufei Xi +5
This paper presents a novel envelope based model predictive control (MPC) framework designed to enable autonomous vehicles to handle high performance driving across a wide range of…