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Dynamic cluster member selection method for multi-target tracking in wireless sensor network

ISSN号：2095-2899
期刊名称：Journal of Central South University
时间：2014.2
页码：636-645
分类：U491.114[交通运输工程—交通运输规划与管理;交通运输工程—道路与铁道工程] U471.14[机械工程—车辆工程;交通运输工程—载运工具运用工程;交通运输工程—道路与铁道工程]
作者机构：[1]Institute of Automation, National University of Defense Technology, Changsha 410073, China
相关基金：Project（90820302） supported by the National Natural Science Foundation of China
相关项目：异质进化算法集成研究

关键词：双车道公路, 长途行车, 感知, 位置, 视觉, 车道检测, 自主车辆, 阳性检出率, lane detection, lane tracking, front vehicle location, full autonomous vehicle, feature line section, autonomous driving, vision

中文摘要：

A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.

英文摘要：

A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads. Firstly, a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways. By using a novel robust lane marking feature which combines the constraints of intensity, edge and width, the lane markings in far regions were extracted accurately and efficiently. Next, the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter. Finally, front vehicles were located on correct lanes using the tracked lane lines. Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%. The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions. This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.

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