In this paper we propose a collocation method for solving Lane-Emden type equation which is nonlinear or-dinary differential equation on the semi-infinite domain. This equation is categorized as singular initial value...In this paper we propose a collocation method for solving Lane-Emden type equation which is nonlinear or-dinary differential equation on the semi-infinite domain. This equation is categorized as singular initial value problems. We solve this equation by the generalized Laguerre polynomial collocation method based on Her-mite-Gauss nodes. This method solves the problem on the semi-infinite domain without truncating it to a fi-nite domain and transforming domain of the problem to a finite domain. In addition, this method reduces so-lution of the problem to solution of a system of algebraic equations.展开更多
【目的】为改进车道线检测算法的精度,优化自动驾驶系统中的车道保持,提出了一种新的车道线检测算法——基于空洞金字塔池化的通道和空间注意力机制的车道线检测算法(channel and position attention mechanism with atrous spatial pyr...【目的】为改进车道线检测算法的精度,优化自动驾驶系统中的车道保持,提出了一种新的车道线检测算法——基于空洞金字塔池化的通道和空间注意力机制的车道线检测算法(channel and position attention mechanism with atrous spatial pyramid pooling for ultra fast structure-aware deep lane detection,CPAM-UFLD)。【方法】首先,在超快速结构感知深度车道检测(ultra fast structure-aware deep lane detection,UFLD)算法的基础上融入了空洞金字塔池化模块(atrous spatial pyramid pooling,ASPP),以有效地捕捉车道线图像中不同尺度的特征;使用了通道和空间注意力机制(channel and position attention mechanism,CPAM)以关注图像中的关键区域;同时,为平衡类别权重和提高定位精度,使用了包括加权交叉熵损失函数等的四种损失函数;其次,提出了一种亮度改善模块,该模块旨在提升输入图像的质量,从而增强车道线的识别度。【结果】本算法在TuSimple数据集上的检测精度由原来的95.86%提升至96.56%;同时,在CULane数据集上,检测精度由原来的72.2%提升至73.7%。【结论】通过算法的改进,可以有效提高车道线检测的精度,这为智能网联汽车自动驾驶系统的环境感知提供了理论参考。展开更多
文摘In this paper we propose a collocation method for solving Lane-Emden type equation which is nonlinear or-dinary differential equation on the semi-infinite domain. This equation is categorized as singular initial value problems. We solve this equation by the generalized Laguerre polynomial collocation method based on Her-mite-Gauss nodes. This method solves the problem on the semi-infinite domain without truncating it to a fi-nite domain and transforming domain of the problem to a finite domain. In addition, this method reduces so-lution of the problem to solution of a system of algebraic equations.
文摘【目的】为改进车道线检测算法的精度,优化自动驾驶系统中的车道保持,提出了一种新的车道线检测算法——基于空洞金字塔池化的通道和空间注意力机制的车道线检测算法(channel and position attention mechanism with atrous spatial pyramid pooling for ultra fast structure-aware deep lane detection,CPAM-UFLD)。【方法】首先,在超快速结构感知深度车道检测(ultra fast structure-aware deep lane detection,UFLD)算法的基础上融入了空洞金字塔池化模块(atrous spatial pyramid pooling,ASPP),以有效地捕捉车道线图像中不同尺度的特征;使用了通道和空间注意力机制(channel and position attention mechanism,CPAM)以关注图像中的关键区域;同时,为平衡类别权重和提高定位精度,使用了包括加权交叉熵损失函数等的四种损失函数;其次,提出了一种亮度改善模块,该模块旨在提升输入图像的质量,从而增强车道线的识别度。【结果】本算法在TuSimple数据集上的检测精度由原来的95.86%提升至96.56%;同时,在CULane数据集上,检测精度由原来的72.2%提升至73.7%。【结论】通过算法的改进,可以有效提高车道线检测的精度,这为智能网联汽车自动驾驶系统的环境感知提供了理论参考。
