3 citations · 3 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size
Thomas Lew, Marcus Greiff, John Subosits +1
Proximal methods such as the Alternating Direction Method of Multipliers (ADMM) are effective at solving constrained quadratic programs (QPs). To tackle infeasible QPs, slack varia…
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…
Control Barrier Functions for Shared Control and Vehicle Safety
James Dallas, John Talbot, Makoto Suminaka +4
This manuscript presents a control barrier function based approach to shared control for preventing a vehicle from entering the part of the state space where it is unrecoverable. T…