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Titel:

Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Benciolini, Tommaso; Tang, Chen; Leibold, Marion; Weaver, Catherine; Tomizuka, Masayoshi; Zhan, Wei
Abstract:
Autonomous racing creates challenging control problems, but Model Predictive Control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-based control, including models of vehicle dynamics and opponent behavior. Both dynamics model error and opponent behavior can be modeled with Gaussian Process (GP) regression. GP models can be updated iteratively from data collected using the control...     »
Zeitschriftentitel:
IEEE Transactions on Control Systems Technology
Jahr:
2024
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1109/TCST.2024.3423630
Semester:
SS 24
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