Autonomous vehicle controllers capable of drifting can improve safety in dynamic emergency situations. However, drifting involves operating at high sideslip angles, which is a fundamentally unstable operating regime that typically requires an accurate vehicle model for reliable operation; such models may not be available after environmental or vehicle parameter changes. Towards that goal, this work presents a Nonlinear Model Predictive Control approach which is capable of initiating and controlling a drift in a production vehicle even when changes in vehicle parameters degrade the original model. A neural network model of the vehicle dynamics is used inside the optimization routine and updated with online learning techniques, giving a higher fidelity and more adaptable model. Experimental validation on a full size, nearly unmodified Lexus LC500 demonstrates the increased modeling fidelity, adaptability, and utility of the presented controller framework. As the LC500 is a difficult car to drift, previous approaches which rely on physics based vehicle models could not complete the autonomous drift tests on this vehicle. Furthermore, the tires on the experimental vehicle are then switched, changing the vehicle parameters, and the capability of the controller to adapt online is demonstrated.

Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially in emergency situations where high forces are required. To this end, we propose a novel family of tire force models based on neural ordinary differential equations and a neural-ExpTanh parameterization. These models are designed to satisfy physically insightful assumptions while also having sufficient fidelity to capture higher-order effects directly from vehicle state measurements. They are used as drop-in replacements for an analytical brush tire model in an existing nonlinear model predictive control framework. Experiments with a customized Toyota Supra show that scarce amounts of driving data – less than three minutes – is sufficient to achieve high-performance autonomous drifting on various trajectories with speeds up to 45mph. Comparisons with the benchmark model show a 4x improvement in tracking performance, smoother control inputs, and faster and more consistent computation time.