I-Corps: Advanced traffic systems and traffic analysis using light detection and ranging (LiDAR) sensors on the roadside

I-Corps：使用路边光检测和测距 (LiDAR) 传感器的先进交通系统和交通分析

• 批准号: 2135414
• 负责人: Hao Xu
• 金额: $ 5万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135414&HistoricalAwards=false
• 关键词: Corps; Advanced; traffic; systems; analysis; Corps; Advanced; traffic; systems; analysis

The broader impact/commercial potential of this I-Corps project is to provide behavior-level data with roadside LiDAR (light detection and ranging) technologies to fill today's traffic data gap and meet data requirements for advanced traffic systems. Advanced traffic engineering requires behavior-level traffic analyses to improve traffic safety, reduce traffic delay, and enhance energy efficiency in transportation. However, existing traffic sensing technologies often cannot provide sufficient data. Potential customers of the envisioned platform, with hardware for data collection and online service for data processing, will primarily be traffic data collection companies that collect and provide required data to government agencies or engineering projects. Larger traffic agencies with in-house data collection teams may also be users of the technology.This I-Corps project further develops a roadside LiDAR (light detection and ranging) traffic sensing system which integrates LiDAR sensing hardware, artificial intelligence (AI) algorithms for data processing, and trajectory analysis services for road traffic. The LiDAR sensor hardware is customizable for both permanent or portable deployment by integrating appropriate power supplies, LiDAR sensors, edge computation units, and data communication devices. The roadside LiDAR sensing platform is significantly different from on-vehicle sensing systems. The AI algorithms are specifically developed for roadside sensing by including background object filtering, object clustering, object classification, object tracking, and geo-referencing of object trajectories for roadside sensing. The geographic information system (GIS) traffic analysis services utilize geo-analysis functions to provide traffic engineering knowledge and/or real-time traffic control input. This project is based on successful demonstration of proof-of-concept applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该I-Corps项目的更广泛的影响/商业潜力是，提供行为级别的数据，以路边LIDAR（光检测和范围）技术填补当今的交通数据差距并满足高级交通系统的数据要求。先进的交通工程需要行为级别的交通分析，以提高交通安全，减少交通延迟并提高运输的能源效率。但是，现有的流量传感技术通常无法提供足够的数据。 设想平台的潜在客户，具有用于数据处理的数据收集和在线服务的硬件，主要是流量数据收集公司，它们为政府机构或工程项目收集并提供所需的数据。拥有内部数据收集团队的较大交通代理商也可能是该技术的用户。该I-Corps项目进一步开发了路边的LIDAR（光检测和范围）交通传感系统，该系统将LIDAR感应硬件（AI）算法集成了用于数据处理的LIDAR感应硬件（AI）算法，以及用于道路交通的轨迹分析服务。 LIDAR传感器硬件可通过整合适当的电源，LIDAR传感器，边缘计算单元和数据通信设备来定制永久性或便携式部署。 路边激光雷达传感平台与车载传感系统有显着不同。 AI算法是专门为路边传感开发的，包括背景对象滤波，对象群集，对象分类，对象跟踪以及路边感应对象轨迹的地理参考。地理信息系统（GIS）流量分析服务利用地理分析功能提供交通工程知识和/或实时交通控制输入。该项目基于概念证明应用程序的成功演示。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评论标准来评估值得支持的。