论文标题
将车道表示为基于电弧长度的参数曲线，以促进车辆控制的估计
Representing Lanes as Arc-length-based Parametric Curves to Facilitate Estimation in Vehicle Control
论文作者
论文摘要
本文将泰勒系列的基本数学重新审视到具有功能表示和基于ARC长度的参数表示的近似曲线。参数表示显示在坐标转换和参数转换中保留其形式。这些防治可以显着促进车辆控制中的车道估计，因为相机感知的车道通常在正在翻译和旋转的车身固定框架中表示。然后，我们得出了从函数表示形式到基于ARC长度的参数表示及其逆的转换。我们将转换应用于车辆控制问题的车道估计，并得出了可用于预测的参数表示系数的演变。我们提出了一个程序，以感知，泳道估计和控制路径跟踪问题的控制。进行模拟以证明使用参数表示所提出的车道估计算法的功效。结果表明，提出的技术可确保车辆控制能够在非常低的感知更新速率下实现合理的良好性能。
This paper revisits the fundamental mathematics of Taylor series to approximate curves with function representation and arc-length-based parametric representation. Parametric representation is shown to preserve its form in coordinate transformation and parameter shifting. These preservations can significantly facilitate lane estimation in vehicle control since lanes perceived by cameras are typically represented in vehicle body-fixed frames which are translating and rotating. Then we derived the transformation from function representation to arc-length-based parametric representation and its inverse. We applied the transformation to lane estimation in vehicle control problem, and derived the evolution of coefficients for parametric representation that can be used for prediction. We come up with a procedure to simulate the whole process with perception, lane estimation and control for the path-following problem. Simulations are performed to demonstrate the efficacy of the proposed lane estimation algorithm using parametric representation. The results indicate that the proposed technique ensures that vehicle control can achieve reasonably good performance at very low perception updating rate.
