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A MonoSLAM approach to lane departure warning system

机译:巷道偏离警告系统的蒙似洲方法

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摘要

Lane Departure Warning (LDW) systems are one of the widely researched topics under Advanced Driver Assistance Systems (ADAS), because they are seen as the most viable way to prevent the traffic accidents caused by involuntary lane departures from happening. Various methods and algorithms used for lane tracking to be used in LDW in the literature; however, most of them only track the lanes or the position of the vehicle inside the lane. This article introduces MonoSLAM based method for LDW design, assuming that the camera is moving in a previously unknown scene. While applying this method, a constant lateral velocity model for the vehicle is used, which assumes that the vehicle is exposed to undetermined Gaussian lateral accelerations. As the first output, the localization of the vehicle on the road is achieved. Moreover, the method is applied with a low cost webcam attached on a vehicle. Five control points for each lane is used to track the lanes and these control points are modelled as if they have a constant position. Detection is made with steerable filters exploiting the state covariance from EKF to make detection more robust. In addition to this, off-line experimental results are given for 200 frames. Results of lane slope on image plane compared with ground truth marked manually for performance benchmarking and localization estimation of a scenario similar to loop closure test is given.
机译:车道出发警告(LDW)系统是先进驾驶辅助系统(ADAS)下的广泛研究的主题之一,因为它们被视为防止由自愿车道偏离发生的交通事故发生的最可行的方式。用于车道跟踪的各种方法和算法用于文献中的LDW;然而,它们中的大多数只能跟踪车道或车道内车辆的位置。本文介绍了基于MonoSlam的LDW设计方法,假设相机在以前未知的场景中移动。在应用该方法的同时,使用用于车辆的恒定横向速度模型,这假设车辆暴露于未确定的高斯横向加速度。作为第一个输出,实现了车辆在道路上的定位。此外,该方法应用于附着在车辆上的低成本网络摄像头。每个车道的五个控制点用于跟踪通道,并且这些控制点被建模,好像它们具有恒定位置。检测采用可控过滤器,利用来自EKF的状态协方差以使检测更加强大。除此之外,离线实验结果为200帧。给出了图像平面车道斜率的结果与手动标记的地面真理进行了绩效基准和定位估计,以及类似于环路闭合试验的情况。

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