Autonomous vehicles capable of safely operating beyond the stable handling limits would be able to perform a broader array of maneuvers during emergencies, thereby enhancing overall safety. This paper presents an approach to autonomous drifting using Koopman-based dynamics models, which globally linearize the nonlinear system dynamics. By leveraging the Koopman operator, we efficiently generate these dynamics models with only 2.2 minutes of offline data. The generated models are integrated with Model Predictive Control to achieve computationally efficient optimization and high tracking accuracy. Additionally, real-time adaptation using online data is employed to reduce model error and enhance trajectory tracking performance. Experimental results demonstrate that the proposed method effectively enables autonomous drifting, offering a significant improvement in control precision and computational efficiency compared to traditional approaches. This work opens up new possibilities for advanced autonomous driving applications where high-speed and precise control are crucial.
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Suminaka et al. (2025) studied this question.