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Vehicle Sideslip Estimation for Four-Wheel-Steering Vehicles Using a Particle Filter

机译:使用粒子滤波器的四轮转向车辆的车辆侧滑估计

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摘要

The availability of the most relevant vehicle states is crucial for the development of advanced vehicle control systems and driver assistance systems. Specifically the vehicle sideslip angle plays a key role, yet this state is unpractical to measure and still not straightforward to estimate. This paper investigates a particle filter approach to estimate the chassis sideslip angle of road vehicles. The filter relies on a physical model of the vehicle and on measurements available from cheap and widespread sensors including inertial measurement unit and steering wheel angle sensor(s). The approach is validated using experimental data collected with the research platform RoboMobil (RoMo), a by-wire electric vehicle with wheel-individual traction and steering actuators. Results show that the performance of the proposed particle filter is satisfactory, and indicate directions for further improvement.
机译:最相关的车辆国家的可用性对于开发先进的车辆控制系统和驾驶员辅助系统至关重要。 具体而言,车辆侧面线角度起着关键作用,但这种状态是不可思议的,仍然不直接估计。 本文研究了估计道路车辆底盘侧线角的粒子过滤方法。 滤波器依赖于车辆的物理模型和可从包括惯性测量单元和方向盘角度传感器的便宜和广泛传感器获得的测量。 使用与研究平台Robomobil(ROMO)的实验数据,带有轮子单个牵引和转向致动器的绕线电动汽车收集的实验数据进行验证。 结果表明,所提出的粒子过滤器的性能令人满意,并表示进一步改善的方向。

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