Mohsen Ibrahim, Ditchi Thierry, Holé Stéphane, Géron Emmanuel
LPEM, PSL Université, ESPCI-Paris, CNRS, 10 rue Vauquelin, 75005 Paris, France.
LPEM, Sorbonne Université, CNRS, 10 rue Vauquelin, 75005 Paris, France.
Sensors (Basel). 2020 Dec 15;20(24):7183. doi: 10.3390/s20247183.
The lateral position of a vehicle in its lane is crucial information required to develop intelligent assistant driving systems. Current studies reveal this information by mixing multiple sources such as cameras, LiDAR or accurateGNSS. Because these systems are not efficient in some degraded weather conditions, a cooperative Vehicle-to-Infrastructure sensor has been developed to help to determine lateral position of a vehicle in its lane. In this paper, the authors propose a completely new and original way to estimate lateral position of the vehicle in its lane using the longitudinal displacement. Using a system based on a hyper-frequency interaction between a transceiver module embedded in the vehicle and passive transponders that can be integrated in the road, for instance under the lane markings, a new signal processing algorithm is presented in order to determine the lateral distance between the vehicle and the transponder axis. The sensor has been tested in an external environment and has shown an estimated lateral distance error of 8 cm at most.
车辆在车道中的横向位置是开发智能辅助驾驶系统所需的关键信息。当前的研究通过融合摄像头、激光雷达或高精度全球导航卫星系统等多种数据源来获取该信息。由于这些系统在某些恶劣天气条件下效率不高，因此开发了一种车路协同传感器来帮助确定车辆在车道中的横向位置。在本文中，作者提出了一种全新的、原创的方法，利用纵向位移来估计车辆在车道中的横向位置。通过使用一种基于车辆嵌入式收发器模块与可集成在道路上（例如在车道标记下方）的无源应答器之间超高频交互的系统，提出了一种新的信号处理算法，以确定车辆与应答器轴之间的横向距离。该传感器已在外部环境中进行测试，结果表明估计的横向距离误差最大为8厘米。
