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Gaussian Processes for Vehicle Dynamics Learning in Autonomous Racing

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

Gaussian Processes for Vehicle Dynamics Learning in Autonomous Racing

Deep Dynamics: Vehicle Dynamics Modeling With a Physics-Constrained Neural Network for Autonomous Racing

This letter introduces Deep Dynamics, a physics-constrained neural network (PCNN) for autonomous racecar vehicle dynamics modeling.

John Chrosniak, Jingyun Ning, Madhur Behl

Deep Dynamics: Vehicle Dynamics Modeling With a Physics-Constrained Neural Network for Autonomous Racing

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

Scalable Deep Kernel Gaussian Process for Vehicle Dynamics in Autonomous Racing

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

Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes

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

Data-Driven Model Predictive Control For Real-Time Stormwater Management