市区车道级导航的自我车道估计

Johannes Rabe, M. Hubner, M. Necker, C. Stiller
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引用次数: 7

摘要

提出了一种用于市区车道级导航的自车道估计算法。它能够可靠地确定当前使用的车道,使用现代生产车辆中可用的传感器,例如里程计、GPS、视觉车道标记检测和基于雷达的物体检测。该方法采用了一种新的粒子滤波方法,将重要权值更新和采样相结合。这一步骤避免了在稀疏粒子集的情况下性能下降,即使与预测的粒子集相比,可能性非常紧密。预处理的里程计数据允许进一步提高性能。在市中心七车道的真实道路上进行的广泛测试中,该系统在95%以上的可用性下,在第95个百分位数内的错误概率低于1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ego-lane estimation for downtown lane-level navigation
We present an ego-lane estimation algorithm for downtown lane-level navigation. It is capable of determining the currently used lane reliably, using sensors available in a modern production vehicle, such as odometry, GPS, visual lane-marking detection, and radar-based object detection. The method employs a particle filter with a novel step that combines the importance weight update and sampling. This step avoids performance deterioration in case of sparse particle sets even when the likelihood is very tight compared to the predicted particle set. Preprocessed odometry data allow for a further performance increase. In an extensive test in downtown scenarios on real roads with up to seven lanes, it achieves error probabilities below 1% in the 95th percentile at availabilities above 95%.
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