Linear System Identification Versus Physical Modeling of Lateral–Longitudinal Vehicle Dynamics

作者：Bernardo A. Hernandez Vicente, Sebastian S. James, Sean Anderson · 发表于：IEEE Transactions on Control Systems Technology · 年份：2020 · DOI：10.1109/tcst.2020.2994120 · 被引用次数：84 · 研究领域：Vehicle Dynamics and Control Systems、Real-time simulation and control systems、Autonomous Vehicle Technology and Safety

Accurate physical modeling of vehicle dynamics requires extensive a priori knowledge of the studied vehicle. In contrast, data-driven modeling approaches require only a set of data that are a good account of the vehicle's driving envelope. In this brief, we compare, for the first time, the prediction capabilities of both approaches applied to a large-scale real-world driving data set. The data set contains several cornering maneuvers, acceleration, and deceleration stages and was collected over public roads. Linear and nonlinear physical models were identified through nonlinear optimization of their unknown parameters. Closed-form subspace identification methods were used to initialize the estimate of a linear state-space model, and the initialization was then refined through nonlinear optimization. The optimized models were validated against 59 km of independent driving data. The model fits, in the longitudinal velocity, were 68.9% versus 80.2% for the nonlinear physical model and linear data-driven (second-order) model, respectively, and, in the yaw rate, 43.0% versus 63.5%. These results show that, for this vehicle, a simple linear data-driven model outperformed both linear and nonlinear physical models under real-world driving conditions. This has important implications for control design approaches in autonomous vehicles.