The knowledge of vehicle dynamical states and parameters plays a crucial role in vehicle stability control systems and, specifically, Vehicle Sideslip Angle (VSA) is an essential factor for active safety control systems. However, the demand for real-time knowledge of this parameter is not practical, due to technical and economic reasons. This paper proposes a novel Interacting Multiple Model Unscented Kalman Filter (IMMUF) to estimate VSA, without tire-road friction coefficient information, and integrating three Unscented Kalman Filters (UKF) to estimate vehicle system models in three different driving conditions (dry, wet, and damp asphalt), characterized by a specific coefficient and modeled through a 2-DOFs single-track vehicle model with a Dugoff tire model. A Monte Carlo analysis has been performed on a wide range of non-trivial driving scenarios and vehicle maneuvers, implemented on a 7-DOFs vehicle model. The results of the estimation have been compared to those of a single UKF, in order to validate the effectiveness of the proposed solution and to highlight the worst performances of a single filter solution in hard driving conditions, justifying the specific Multiple Model solution adopted.
Enhancing ADS and ADAS Under Critical Road Conditions Through Vehicle Sideslip Angle Estimation via Unscented Kalman Filter-Based Interacting Multiple Model Approach / Battistini, S.; Brancati, R.; Lui, D. G.; Tufano, F.. - 122:(2022), pp. 450-460. (Intervento presentato al convegno 4th International Conference of the IFToMM Italy, IFIT 2022 tenutosi a ita nel 2022) [10.1007/978-3-031-10776-4_52].
Enhancing ADS and ADAS Under Critical Road Conditions Through Vehicle Sideslip Angle Estimation via Unscented Kalman Filter-Based Interacting Multiple Model Approach
Brancati R.;Lui D. G.;Tufano F.
2022
Abstract
The knowledge of vehicle dynamical states and parameters plays a crucial role in vehicle stability control systems and, specifically, Vehicle Sideslip Angle (VSA) is an essential factor for active safety control systems. However, the demand for real-time knowledge of this parameter is not practical, due to technical and economic reasons. This paper proposes a novel Interacting Multiple Model Unscented Kalman Filter (IMMUF) to estimate VSA, without tire-road friction coefficient information, and integrating three Unscented Kalman Filters (UKF) to estimate vehicle system models in three different driving conditions (dry, wet, and damp asphalt), characterized by a specific coefficient and modeled through a 2-DOFs single-track vehicle model with a Dugoff tire model. A Monte Carlo analysis has been performed on a wide range of non-trivial driving scenarios and vehicle maneuvers, implemented on a 7-DOFs vehicle model. The results of the estimation have been compared to those of a single UKF, in order to validate the effectiveness of the proposed solution and to highlight the worst performances of a single filter solution in hard driving conditions, justifying the specific Multiple Model solution adopted.File | Dimensione | Formato | |
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