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A MonoSLAM approach to lane departure warning system

机译:MonoSLAM车道偏离预警系统的方法

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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:车道偏离警告(LDW)系统是高级驾驶员辅助系统(ADAS)之下被广泛研究的主题之一,因为它们被视为防止因非自愿偏离车道而引起的交通事故的最可行方法。文献中用于LDW的用于车道跟踪的各种方法和算法;但是,它们中的大多数仅跟踪车道或车辆在车道内的位置。本文介绍了基于MonoSLAM的LDW设计方法,假设摄像机在先前未知的场景中移动。在应用此方法时,将使用车辆的恒定横向速度模型,该模型假定车辆暴露于不确定的高斯横向加速度。作为第一输出,实现了车辆在道路上的定位。此外,该方法与附接在车辆上的低成本网络摄像头一起应用。每个车道使用五个控制点来跟踪车道,并对这些控制点进行建模,就好像它们具有恒定的位置一样。使用可控滤波器进行检测,并利用来自EKF的状态协方差使检测更可靠。除此之外,还给出了200帧的离线实验结果。给出了在图像平面上的车道坡度与手动标记的地面真实情况相比较的结果,用于性能基准测试和类似于闭环测试的场景的本地化估计。

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