NeTS: Small: Infrastructure-free Robust Relative Localization of Vehicles on the Road

NeTS：小型：道路上车辆的无基础设施的鲁棒相对定位

• 批准号: 1618520
• 负责人: Kannan Srinivasan
• 金额: $ 51.6万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618520&HistoricalAwards=false
• 关键词: NeTS; Small; Infrastructure; free; Robust

The automotive industry is undergoing a giant transformation as vehicles are gearing up to take control of driving away from humans. This will lead to reduced chance of accidents, stress-free driving, increased passenger comfort, increased fuel-efficiency and reduced travel time. One of the most critical pieces of information for enabling this vision of autonomous vehicles is accurate determination of locations of nearby vehicles. The overarching goal of this project is to develop robust approaches that are practically implementable for fine-grained relative localization of nearby vehicles with no support from the infrastructure. In particular, on-board sensing capabilities and RF (radio-frequency) communication between vehicles will be leveraged. The key distinguishing features of the project are as follows: 1) a comprehensive approach to the problem of fine-grained vehicular localization while considering legacy vehicles which is the most critical hurdle in rolling out autonomous vehicles; 2) innovative physical layer techniques to achieve high accuracy relative localization; and, 3) techniques to use noisy data collected by various sensors and still robustly derive the locations of the vehicles. The project is potentially transformative as it addresses the key question of accurate relative localization of vehicles while considering practical challenges, which is crucial for the autonomous driving technology. The broader impacts include enhancing undergraduate and graduate curriculum. In addition, existing collaboration with the automotive industry will be effectively leveraged for obtaining guidance on the objectives and understanding the feasibility of our solutions throughout the project.The specific inter-related research thrusts are as follows. 1) Frequency-Pair based Analysis for Single Antenna Vehicles: The key innovation is an approach that can robustly tackle the distance estimation error and the vagaries of a real driving environment using analysis of frequency-pairs. 2) Exploiting Spatial Diversity in Multi-Antenna Vehicles: The key innovation of this thrust is that it uses the spatial diversity offered by multiple antennas to increase robustness. 3) Collaborative Vehicle-Map Construction in Presence of Legacy Vehicles: The key innovation is to robustly fuse noisy information obtained through cameras mounted on the participating vehicles.

汽车行业正在经历一场巨大的变革，因为汽车正准备从人类手中接管驾驶控制权。这将减少事故发生的机会，无压力驾驶，提高乘客舒适度，提高燃油效率和减少旅行时间。实现自动驾驶车辆视觉的最关键信息之一是准确确定附近车辆的位置。该项目的总体目标是开发强大的方法，这些方法实际上可以在没有基础设施支持的情况下实现附近车辆的细粒度相对定位。特别是，将利用车载传感能力和车辆之间的RF（射频）通信。该项目的主要特点如下：1）全面解决细粒度车辆定位问题，同时考虑传统车辆，这是推出自动驾驶车辆的最关键障碍; 2）创新的物理层技术，以实现高精度相对定位;以及3）使用由各种传感器收集的噪声数据并且仍然稳健地导出车辆位置的技术。该项目具有潜在的变革性，因为它解决了车辆精确相对定位的关键问题，同时考虑了实际挑战，这对自动驾驶技术至关重要。更广泛的影响包括加强本科生和研究生课程。此外，我们还将有效利用与汽车行业的现有合作，以获得有关目标的指导，并在整个项目中了解我们解决方案的可行性。具体的相互关联的研究重点如下。1)单天线车辆的基于频率对的分析：关键创新是一种方法，该方法可以使用频率对分析来鲁棒地解决距离估计误差和真实的驾驶环境的变幻莫测。2)在多天线车辆中利用空间分集：该推力的关键创新在于它使用多个天线提供的空间分集来增加鲁棒性。3)在传统车辆存在的情况下协作车辆地图构建：关键的创新是鲁棒地融合通过安装在参与车辆上的摄像头获得的噪声信息。