Cáceres Hernández Danilo, Kurnianggoro Laksono, Filonenko Alexander, Jo Kang Hyun
Intelligent Systems Laboratory, Graduate School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea.
Sensors (Basel). 2016 Nov 17;16(11):1935. doi: 10.3390/s16111935.
Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance.
在过去几十年里，路面标线在诸如引导、导航和控制等智能车辆应用中发挥了关键作用。然而，车道标线检测问题仍面临严峻挑战。例如，存在处理时间过长以及因交通标志（导流线、停止线、人行横道、箭头等）颜色和边缘相似而导致误检测等问题。本文提出一种策略，该策略考虑车道标线的颜色、边缘、宽度以及车速等特征来提取车道标线信息。首先，定义感兴趣区域是实现实时性能的关键任务。从这个意义上讲，感兴趣区域取决于车速。其次，基于距离 - 颜色相关性和分层拟合模型，使用混合颜色 - 边缘特征方法结合概率方法来检测车道标线。第三，提取以下车道标线信息：车辆两侧的车道标线数量、各自的拟合模型以及车道的质心信息。利用这些参数，通过道路几何模型计算区域。为评估所提方法，使用了一组连续帧来验证性能。
