The knowledge of key vehicle states is crucial to guarantee adequate safety levels for modern passenger cars, for which active safety control systems are lifesavers. In this regard, vehicle sideslip angle is a pivotal state for the characterization of lateral vehicle behavior. However, measuring sideslip angle is expensive and unpractical, which has led to many years of research on techniques to estimate it instead. This paper presents a novel method to estimate vehicle sideslip angle, with an innovative combination of a kinematic-based approach and a dynamic-based approach: part of the output of the kinematic-based approach is fed as input to the dynamic-based approach, and vice-versa. The dynamic-based approach exploits an Unscented Kalman Filter (UKF) with a double-track vehicle model and a modified Dugoff tire model, that is simple yet ensures accuracy similar to the well-known Magic Formula. The proposed method is successfully assessed on a large amount of experimental data obtained on different race tracks, and compared with a traditional approach presented in the literature. Results show that the sideslip angle is estimated with an average error of 0.5 deg, and that the implemented cross-combination allows to further improve the estimation of the vehicle longitudinal velocity compared to current state-of-the-art techniques, with interesting perspectives for future onboard implementation.

关键车辆状态的知识对于保证现代乘用车足够的安全水平至关重要，主动安全控制系统是现代乘用车的救星。在这方面，车辆侧滑角是表征车辆横向行为的关键状态。然而，测量侧滑角既昂贵又不切实际，这导致了多年来对侧滑角估计技术的研究。本文提出了一种估计车辆侧滑角的新方法，它创新地结合了基于运动学的方法和基于动力学的方法：基于运动学的方法的部分输出作为基于动力学的方法的输入，反之亦然。基于动态的方法利用无迹卡尔曼滤波器 (UKF) 和双轨车辆模型和改进的 Dugoff 轮胎模型，这很简单，但可确保类似于众所周知的魔术公式的准确性。所提出的方法成功地评估了在不同赛道上获得的大量实验数据，并与文献中提出的传统方法进行了比较。结果表明，侧滑角的估计平均误差为 0.5 度，与当前最先进的技术相比，所实施的交叉组合允许进一步改进对车辆纵向速度的估计，具有有趣的视角未来的船上实施。