aiqbench analysis
This research introduces a hybrid prediction model combining kinematic, Bayesian, and Gaussian process regression to significantly enhance Model Predictive Control (MPC) for autonomous race cars operating near physical limits. By integrating historical data with real-time conditions, the approach achieves up to 57% improved prediction accuracy while maintaining low computational costs suitable for real-time deployment. The study validates the practical applicability of this method on a physical Formula Student vehicle, offering a scalable framework for handling highly nonlinear vehicle dynamics in constrained environments.
Vehicle Prediction Model for Enhanced MPC Path Tracking in Formula Student Driverless
arXiv:2606.10732v1 Announce Type: new Abstract: Autonomous race cars, such as in Formula Student Driverless, operate close to their physical handling limits. The resulting highly nonlinear vehicle behavior increases the path tracking complexity, especially on narrow tracks. Model Predictive Control (MPC) is commonly used to address this issue, a method whose performance is closely tied to the accuracy of the underlying prediction model. This paper presents a novel, real-time capable prediction model for autonomous race cars that adjusts to changing conditions by combining information from past runs and the current driving situation. Our model is divided into three consecutive submodels: a nominal Kinematic Bicycle Model, an offline Bayesian Linear Regression (BLR) model, and an online Sparse Gaussian Process Regression (SGPR) model. The proposed approach enables efficient integration of all available data without significantly increasing computational cost, ensuring high prediction accuracy and a quantitative uncertainty assessment right from the start of the run. Compared to existing approaches, an improvement in prediction accuracy of up to 57% was achieved. Further, we successfully demonstrated the practical applicability of the model within an MPC-based path tracking controller on a real Formula Student race car.
Abstract:Autonomous race cars, such as in Formula Student Driverless, operate close to their physical handling limits. The resulting highly nonlinear vehicle behavior increases the path tracking complexity, especially on narrow tracks. Model Predictive Control (MPC) is commonly used to address this issue, a method whose performance is closely tied to the accuracy of the underlying prediction model. This paper presents a novel, real-time capable prediction model for autonomous race cars that adjusts to changing conditions by combining information from past runs and the current driving situation. Our model is divided into three consecutive submodels: a nominal Kinematic Bicycle Model, an offline Bayesian Linear Regression (BLR) model, and an online Sparse Gaussian Process Regression (SGPR) model. The proposed approach enables efficient integration of all available data without significantly increasing computational cost, ensuring high prediction accuracy and a quantitative uncertainty assessment right from the start of the run. Compared to existing approaches, an improvement in prediction accuracy of up to 57% was achieved. Further, we successfully demonstrated the practical applicability of the model within an MPC-based path tracking controller on a real Formula Student race car.
Robotics (cs.RO)
arXiv:2606.10732 [cs.RO]
(or arXiv:2606.10732v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.10732 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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