The vehicle sideslip angle is an important state for vehicle dynamic control, which needs to be estimated as it could not be obtained directly by the vehicle. To improve the estimation accuracy of the sideslip angle based on the intelligent vehicle platform, this study proposes a novel vehicle sideslip angle estimation algorithm with the fusion of dynamic model and vision information. Firstly, to further improve the model accuracy of the vehicle during lateral acceleration conditions, a vehicle dynamic model is established considering the acceleration error compensation with the assistance of attitude information. In addition, based on the lane line information obtained from the equipped camera in intelligent vehicles, a visual geometric model is established. Owing to the measurement delay and low sampling frequency of the camera, a multi-rate sideslip angle observer with delay compensation is designed to coordinate with the inter-frequency signal of the vehicle chassis. Finally, the effectiveness of the algorithm is verified by the slalom test.

中文翻译：

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基于动态模型的视觉辅助智能汽车侧滑角估计

车辆侧滑角是车辆动态控制的重要状态，由于不能直接由车辆获得，因此需要估算。为了提高基于智能车辆平台的侧滑角估计精度，提出了一种融合了动力学模型和视觉信息的新型车辆侧滑角估计算法。首先，为了进一步提高车辆在横向加速度条件下的模型精度，在考虑姿态信息的情况下考虑加速度误差补偿来建立车辆动力学模型。另外，基于从智能车辆中配备的摄像机获得的车道线信息，建立视觉几何模型。由于相机的测量延迟和较低的采样频率，设计了具有延迟补偿的多速率侧滑角观测器，以与车辆底盘的异频信号协调。最后，通过激流回旋测试验证了算法的有效性。