Fused Front Lane Trajectory Estimation Based on Current ADAS Sensor Configuration

Yuchen Liu, Haoyang Cheng, Zhiqiang Li
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Abstract

Intelligent driving functions, such as ACC (Adaptive Cruise Control) and ALC (Automated Lane Changes), require lane assignment for objects. It relies on an accurate traffic lane path estimation. This paper proposes a fused front lane trajectory estimation algorithm based on current common ADAS sensor configuration. This trajectory is generated by fusing information of lane markers, front object trails and host motion state. This algorithm uses a clothoid lane model and its coefficients is estimated by a Kalman Filter, which weighs predicted model state and current measurement. This approach is verified by a set of real road test data.
基于当前ADAS传感器配置的融合前车道轨迹估计
智能驾驶功能,如ACC(自适应巡航控制)和ALC(自动变道),需要为物体分配车道。它依赖于精确的交通车道路径估计。本文提出了一种基于当前常用ADAS传感器配置的融合前车道轨迹估计算法。该轨迹融合车道标记、前方目标轨迹和主体运动状态信息生成。该算法采用仿线通道模型,通过卡尔曼滤波估计其系数,并对预测模型状态和当前测量值进行加权。通过一组真实道路试验数据验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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