A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and...A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.展开更多
Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning...Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.展开更多
提出了一种在RO I(region of interest)即感兴趣区域中建立搜索窗口的车道识别算法,使其能够很好地处理非理想路况的各种不确定因素,并建立实时动态搜索窗口,除了能精确地追踪车道标志线之外,还能应用于弯路路况。运用LM edSquare曲线...提出了一种在RO I(region of interest)即感兴趣区域中建立搜索窗口的车道识别算法,使其能够很好地处理非理想路况的各种不确定因素,并建立实时动态搜索窗口,除了能精确地追踪车道标志线之外,还能应用于弯路路况。运用LM edSquare曲线拟合算法提高了车道拟合过程的稳健性和精确性。由于RO I的建立,整个识别过程的计算量大大降低,适应实时性要求。展开更多
基金Project(51175159)supported by the National Natural Science Foundation of ChinaProject(2013WK3024)supported by the Science andTechnology Planning Program of Hunan Province,ChinaProject(CX2013B146)supported by the Hunan Provincial InnovationFoundation for Postgraduate,China
文摘A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.
基金financially supported by the National Natural Science Foundation of China(grant No.61170147)the Scientific and Technological Project of Shaanxi Province in China(grant No.2019GY-038)。
文摘Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.
