Laconte Johann, Kasmi Abderrahim, Aufrère Romuald, Vaidis Maxime, Chapuis Roland
Clermont Auvergne INP, CNRS, Institut Pascal, Université Clermont Auvergne, F-63000 Clermont-Ferrand, France.
Northern Robotics Laboratory, Université Laval, Quebec, QC G1V 0A6, Canada.
Sensors (Basel). 2021 Dec 30;22(1):247. doi: 10.3390/s22010247.
In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle's position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods.
在高速公路自动驾驶车辆的背景下，首要且最重要的任务之一是确定车辆在道路上的位置。为此，车辆需要能够考虑来自多个传感器的信息，并将它们与来自道路地图的数据进行融合。高速公路上的定位问题可归纳为三个主要部分。第一部分是推断车辆当前行驶在哪条道路上。实际上，全球导航卫星系统自身不够精确，无法单独推断出此信息，因此需要一个滤波步骤。第二部分是估计车辆在其车道内的位置。最后，第三也是最后一部分旨在评估车辆当前行驶在哪条车道上。这最后两个部分对于安全驾驶是必不可少的，因为诸如超车等操作需要有关车辆当前位置的精确信息。在本次综述中，我们介绍了高速公路场景下自动驾驶车辆定位方法的分类。我们阐述了定位过程的每个主要部分，并讨论了相关的最新方法的优缺点。
