Advanced control and driving assistance systems play a major role in modern vehicles, ensuring higher standards of safety and performance. Their correct operation extensively depends on the knowledge of tyre forces and vehicle drift. However, these quantities are hard to measure directly, due to cost or technological reasons. One possible alternative that is attracting much attention in the last few years is represented by virtual sensing where the quantities of interest can be inferred using a physical model that maps the relationship between these quantities and other available direct measurements, like accelerations, velocities and rate-of-turns. In this research, model-based observation is adopted to predict tyre forces and slip angles. In contrast to existing systems, ours relies on direct causality equations without the need of any explicit tyre model. Different observers are developed that are grounded, respectively, in the Cubature Kalman and Particle filtering, and they are contrasted against the standard Extended Kalman filter (EKF). Results are presented to quantitatively assess the performance of the observers using a 14 Degrees Of Freedom (DOFs) full vehicle model that has been simulated in standard manoeuvres including constant radius cornering, increasing and swept-sine steering, and sine-dwell manoeuvring. Although all three embodiments allow model nonlinearities and measurement noise to be appropriately tackled, the two Kalman filters outperform the PF in terms of estimation accuracy, especially for tyre force prediction. In addition, the novel Cubature Kalman filter shows comparable accuracy and robustness, but higher stability when compared to the EKF.

先进的控制和驾驶辅助系统在现代车辆中发挥着重要作用，确保更高的安全和性能标准。它们的正确操作很大程度上取决于轮胎力和车辆漂移的知识。然而，由于成本或技术原因，这些数量很难直接测量。在过去几年中引起广泛关注的一种可能的替代方法是虚拟传感，其中可以使用物理模型推断感兴趣的量，该物理模型映射这些量与其他可用直接测量值之间的关系，如加速度、速度和速率-轮流。在这项研究中，采用基于模型的观察来预测轮胎力和侧偏角。与现有系统相比，我们的依赖于直接因果关系方程，不需要任何明确的轮胎模型。开发了不同的观测器，它们分别基于 Cubature Kalman 和 Particle 滤波，并将它们与标准扩展卡尔曼滤波器 (EKF) 进行对比。使用 14 自由度 (DOF) 整车模型对观察者的性能进行定量评估，该模型已在标准机动中进行了模拟，包括恒定半径转弯、增加和扫掠正弦转向以及正弦驻留机动。尽管所有三个实施例都允许适当地处理模型非线性和测量噪声，但两个卡尔曼滤波器在估计精度方面优于 PF，特别是对于轮胎力预测。此外，