To address the failure to consider vehicle states in region of interest (ROI) prediction, we propose the use of a Kalman filter to estimate the position of vehicles relative to lanes by vehicle states on the basis of a vehicle–road micro traffic model in the world coordinate system. The central position of the ROI is determined through a combination of optimal preview time theory with the ROI prediction. The range of the ROI is determined by offsetting upward, downward, leftward, and rightward from the central position of the ROI. The left and right ROI are processed separately to detect lane lines. Simulation results show that the proposed prediction method reduces the ROI range, and the model predictive control controller can make the vehicle run smoothly from the initial position to the road centerline.

中文翻译：

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考虑车辆运动状态的感兴趣区域预测下基于模型预测控制的车道保持控制

为解决未能在关注区域（ROI）预测中考虑车辆状态的问题，我们建议使用卡尔曼滤波器根据车辆状态中道路微交通模型估算车辆相对于车道的位置。世界坐标系。通过将最佳预览时间理论与ROI预测相结合，可以确定ROI的中心位置。通过从ROI的中心位置向上，向下，向左和向右偏移来确定ROI的范围。左和右ROI分别进行处理以检测车道线。仿真结果表明，所提出的预测方法减小了ROI范围，并且模型预测控制控制器可使车辆从初始位置到道路中心线平稳行驶。