3 citations · 3 across the 14 of their papers we have counts for
15 papers
Emergent Autonomous Drifting for Collision Avoidance in Real-World Winter Driving Scenarios
Elliot Weiss, Michael Thompson, Thomas Lew +1
Real-world collision avoidance is a core motivation for studying the dynamics and control of high sideslip drifting in vehicles, yet the practical benefit of such maneuvers has so…
3D Lane Detection with Odometry for High-Speed Vehicle Racing
Omoruyi Atekha, John Subosits, Marcus Greiff
Lane boundary detection is a critical component in autonomous driving systems and has been rigorously studied in regular driving scenarios. However, it is less explored in vehicle…
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
Ray Zhang, Marcus Greiff, Thomas Lew +1
We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings. T…
Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
James Dallas, Thomas Lew, John Talbot +3
Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance…
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…
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…
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
Alexander Davydov, Franck Djeumou, Marcus Greiff +4
Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. However, when deployed on unstable systems, on…
Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies
Franck Djeumou, Michael Thompson, Makoto Suminaka +1
The skill to drift a car--i.e., operate in a state of controlled oversteer like professional drivers--could give future autonomous cars maximum flexibility when they need to retain…
Risk-Averse Model Predictive Control for Racing in Adverse Conditions
Thomas Lew, Marcus Greiff, Franck Djeumou +3
Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In particular, the performance of MPC for vehicle con…