Jingyun Ning
Jingyun Ning
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Gaussian Processes for Vehicle Dynamics Learning in Autonomous Racing (In progress)
This journal article provides a comprehensive overview and synthesis of prior research efforts focused on the development and refinement of Gaussian Process (GP) models for the purpose of learning and understanding vehicle dynamics.
Jingyun Ning
,
Madhur Behl
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Scalable Deep Kernel Gaussian Process for Vehicle Dynamics in Autonomous Racing
We investigated the potential of DKL-SKIP as a promising tool for modeling complex vehicle dynamics in both real-world and simulated environments.
Jingyun Ning
,
Madhur Behl
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Poster
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes
We conduct 400+ simulations on real F1 track layouts to provide comprehensive recommendations to the research community for training accurate GP regression for single-track vehicle dynamics of a racecar.
Jingyun Ning
,
Madhur Behl
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Data-Driven Model Predictive Control For Real-Time Stormwater Management
We propose a data-driven approach, which utilizes unstructured state-space models for system identification and predictive control implementation.
Jingyun Ning
,
Benjamin D Bowes
,
Jonathan L Goodall
,
Madhur Behl
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