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Keeping the Vehicle on the Road - A Survey on On-Road Lane Detection Systems

机译：保持车辆行驶-道路车道检测系统调查

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摘要

The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.
机译：无线传感器网络的发展，例如研究人员高级驾驶员辅助系统（ADAS），需要像人类一样分析道路场景的能力。道路场景分析是一项必不可少，复杂且具有挑战性的任务，它包括：道路检测（包括道路的定位，确定车辆与道路之间的相对位置以及分析车辆的前进方向）和障碍物检测（主要基于定位车辆路径上可能的障碍物）。在这种情况下，道路边界的检测，道路几何形状的估计以及车辆的定位是必不可少的任务，因为它们是车辆的横向和纵向控制所必需的。在该领域内，车载视觉已被广泛使用，因为它具有比其他有源传感器（如RADAR或LIDAR）更多的优势（分辨率更高，功耗更低，成本更低，易于美学整合以及非侵入性）。乍一看，从视觉信息中检测道路几何形状的问题似乎很简单，该领域的早期工作很快获得了可喜的成果。但是，行业要求的各种场景和高成功率使车道检测研究工作保持活跃。在本文中，对基于视觉的道路检测系统视觉进行了全面回顾。

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