The knowledge of Vehicle Sideslip Angle (VSA) can play an essential role in active safety vehicle control systems. However, due to the high costs of sensing instruments, this information is difficult to be directly measured onboard of series production vehicles, restricting its application in practice. It follows that there is a need for online VSA estimation methods only based on available measurements from low-cost sensors. From this perspective, this work proposes a strategy based on Interacting Multiple Model (IMM) filters, which does not require tyre–road friction coefficient knowledge. By integrating the available onboard sensor data, the IMM estimates relevant information in different driving conditions leveraging a 2-Degrees Of Freedom (DOF) single-track vehicle model embedding a Dugoff tyre representation. Two alternative IMM algorithms based on the Extended (EKF) and Unscented Kalman filter (UKF) are developed. Moreover, while usually the transition probabilities among models in classical IMMs are fixed and set on prior information and/or dedicated analysis, here these conservative hypotheses are relaxed introducing a state-dependent Markov transition matrix based on a novel model switching algorithm. The effectiveness of the new proposed methods is evaluated on extensive non-trivial simulation scenarios through a Monte Carlo analysis exploiting an accurate 15-DOF vehicle model via a purposely designed high-fidelity co-simulation platform embedding the dSPACE software Automotive Simulation Model (ASM). Results provide a meaningful comparative performance analysis between the IMMEKF and IMMUKF solutions, as well as with respect to traditional IMM based on constant probabilities transition matrix, blue in both the EKF and UKF configuration. Finally, the developed IMM-based estimation strategy is tested in two realistic driving scenarios to assess the VSA estimation accuracy in case of abrupt changes in road surface conditions.

中文翻译：

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通过基于非线性卡尔曼滤波器的状态相关交互多模型方法估计关键道路条件下的车辆侧滑角

车辆侧滑角 (VSA) 的知识可以在主动安全车辆控制系统中发挥重要作用。然而，由于传感仪器成本高昂，这些信息很难在量产车辆上直接测量，限制了其在实际中的应用。因此，需要仅基于低成本传感器的可用测量值的在线 VSA 估计方法。从这个角度来看，这项工作提出了一种基于交互多模型（IMM）过滤器的策略，该策略不需要轮胎-道路摩擦系数知识。通过集成可用的车载传感器数据，IMM 利用嵌入 Dugoff 轮胎表示的 2 自由度 (DOF) 单轨车辆模型来估计不同驾驶条件下的相关信息。开发了两种基于扩展 (EKF) 和无迹卡尔曼滤波器 (UKF) 的替代 IMM 算法。此外，虽然经典 IMM 中模型之间的转移概率通常是固定的，并根据先验信息和/或专用分析进行设置，但这里这些保守的假设被放松，引入了基于新颖模型切换算法的状态相关马尔可夫转移矩阵。通过蒙特卡罗分析，通过特意设计的嵌入 dSPACE 软件汽车仿真模型 (ASM) 的高保真联合仿真平台，利用精确的 15 自由度车辆模型，在广泛的非平凡仿真场景中评估新提出方法的有效性。结果提供了 IMMEKF 和 IMMUKF 解决方案之间以及相对于基于恒定概率转移矩阵（EKF 和 UKF 配置中均为蓝色）的传统 IMM 的有意义的性能比较分析。最后，在两个实际驾驶场景中测试了所开发的基于 IMM 的估计策略，以评估在路面条件突然变化的情况下 VSA 估计的准确性。