An iterative neural-network-corrected system identification method can estimate Pacejka tire parameters on a racing track in under a minute, matching steady-state identification accuracy and outperforming nonlinear least squares under noise.
Deep dynamics: Vehicle dynamics modeling with a physics-constrained neural network for autonomous racing,
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Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
An iterative neural-network-corrected system identification method can estimate Pacejka tire parameters on a racing track in under a minute, matching steady-state identification accuracy and outperforming nonlinear least squares under noise.