应用鲁棒卡尔曼滤波的车道线跟踪方法OA
Lane Line Tracking Method Using Robust Kalman Filter
为避免车道线参数因遮挡、磨损等因素而引起的检测误差,本文提出一种应用鲁棒卡尔曼滤波的车道线跟踪方法.该方法基于提取到的车道线离散点数据,采用回旋曲线方程构建车道线参数模型.通过采样预处理获取的均匀分布散点数据推导出卡尔曼滤波的观测方程.同时结合车辆的运动状态数据,推导卡尔曼滤波的预测方程.并且基于观测方程的残差,对系统过程噪声的协方差矩阵进行自适应计算.实验结果显示,本文方法计算得到的左右车道线横向误差均值小于9cm,最大值不超过25cm,满足智能驾驶场景的功能需要.
To avoid detection errors caused by factors such as obstruction and wear of lane line parameters,this paper proposes a lane line tracking method using robust Kalman filtering.This method is based on the extracted discrete point data of lane markings and uses the cyclotron curve equation to construct a lane marking parameter model.Derive the observation equation of Kalman filter from uniformly distributed scatter data obtained through sampling preprocessing.Simultaneously combining the motion state data of the vehicle,derive the prediction equation of Kalman filter.And based on the residuals of the observation equation,adaptively calculate the covariance matrix of the system process noise.The experimental results show that the average lateral error of the left and right lane lines calculated by the method in this article is less than 9cm,and the maximum value does not exceed 25cm,which meets the functional requirements of intelligent driving scenarios.
杨勤峰;金贵阳;于磊磊;孙德盟;秦建波
宁波职业技术学院智能装备研究所 浙江 宁波 315800宁波职业技术学院智能装备研究所 浙江 宁波 315800宁波职业技术学院智能装备研究所 浙江 宁波 315800宁波职业技术学院智能装备研究所 浙江 宁波 315800上海商汤科技开发有限公司智能驾驶部门 上海 201600
信息技术与安全科学
车道线跟踪卡尔曼滤波智能驾驶
Lane Line TrackingKalman FilterIntelligent Driving
《福建电脑》 2025 (7)
12-16,5
本文得到宁波职业技术学院2025年度校级课题(No.NZ25018)资助.
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