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

I-Corps项目的更广泛影响/商业潜力是通过路边激光雷达（光探测和测距）技术提供行为级数据，以填补当今交通数据的空白，并满足先进交通系统的数据需求。先进的交通工程需要行为层面的交通分析，以提高交通安全，减少交通延误，提高交通运输的能源效率。然而，现有的交通传感技术往往不能提供足够的数据。这个设想中的平台拥有用于数据收集的硬件和用于数据处理的在线服务，其潜在客户将主要是交通数据收集公司，这些公司收集并向政府机构或工程项目提供所需的数据。拥有内部数据收集团队的大型交通机构也可能是该技术的用户。I-Corps项目进一步开发了路边激光雷达（光探测和测距）交通传感系统，该系统集成了激光雷达传感硬件、用于数据处理的人工智能（AI）算法和道路交通轨迹分析服务。通过集成适当的电源、激光雷达传感器、边缘计算单元和数据通信设备，激光雷达传感器硬件可定制为永久或便携式部署。路边激光雷达传感平台与车载传感系统有很大不同。人工智能算法是专门为路边传感开发的，包括背景目标过滤、目标聚类、目标分类、目标跟踪和路边传感目标轨迹的地理参考。地理信息系统（GIS）交通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。该项目基于概念验证应用程序的成功演示。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.