aiqbench analysis
This research advances autonomous vehicle control by integrating offline Bayesian Linear Regression with online Sparse Gaussian Process Regression to significantly improve prediction accuracy for Model Predictive Control systems in highly nonlinear driving scenarios. The approach successfully balances computational efficiency with the need for real-time uncertainty assessment, offering a practical solution for high-performance robotics and self-driving applications operating near physical limits.
Vehicle Prediction Model for Enhanced MPC Path Tracking in Formula Student Driverless
arXiv:2606.10732v2 Announce Type: replace 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:2606.10732v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.10732 Focus to learn more arXiv-issued DOI via DataCite
2026 IEEE Intelligent Vehicles Symposium (IV), 2026, pp. 1571-1577
https://doi.org/10.1109/IV66570.2026.11624038 Focus to learn more DOI(s) linking to related resources
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