I-Corps: Advanced Truck Detection with Lidar Technology

这个I-Corps项目的更广泛的影响/商业潜力是使运输机构能够使用被动收集和匿名传感器收集关键的货运数据。 敏捷性对于成功收集运输数据集至关重要，特别是在竞争激烈的货运行业。 传统的方法，如基于图像的检测，手机跟踪或其他视觉监控（车牌跟踪或徽标识别）可能会违反隐私考虑，并阻碍广泛的货运数据收集。 这种数据收集对于出行需求建模和预测以及道路、桥梁和货运码头的基础设施规划、运营和维护都是必要的。 这种先进的卡车检测设备的市场包括市、县、州和国家一级的公共交通机构、交通传感设备制造商、运输咨询公司和货运码头运营商。 虽然目前的传感器可以通过车轴配置将卡车与汽车或卡车区分开，但是目前还没有非路面侵入技术能够足够详细地预测车辆的车身类型以指示所运载的货物。 负责收集数据的机构往往必须依靠耗时的定期调查来估计其公路系统上的货物流向和运输情况，因此难以及时进行项目成本效益和复原力/影响分析。 商业应用可以扩展到大型配送中心、矿区、铁路站场或其他多式联运码头和港口。I-Corps项目将通过开发基于侧射（垂直于交通流）激光雷达（光检测和测距）的交通检测和分类系统，进一步开发一种低成本、匿名和非侵入性的先进卡车检测系统。 在侧射配置中，激光雷达传感器捕获卡车（牵引车和拖车/半拖车）的轮廓，可以通过高分辨率的车身类型进行分类，同时保持驾驶员，执照/注册和公司的匿名性。 该技术的创新包括：1）用于交通检测的现成激光雷达技术的新颖配置和应用，2）将技术与机器学习算法相结合，用于特征检测，提取和分类，旨在实现高分辨率卡车分类，以及3）在数据仪表板中实现分类输出，用于真实的时间和历史回顾。这一使用激光雷达的新型卡车检测解决方案可以从根本上改变货运数据的收集方式，特别是公共交通机构。该奖项反映了NSF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 enable transportation agencies to gather critical freight movement data using passively collected and anonymous sensors. Anonymity is of key importance for the successful collection of transportation datasets, especially in the competitive freight industry. Traditional approaches such as image-based detection, cell phone tracking, or other visual monitoring (license plate tracking or logo recognition) can violate privacy considerations and hinder widespread freight data collection. Such data collection is necessary for travel demand modeling and forecasting as well as for infrastructure planning, operations, and maintenance for roadways, bridges, and freight terminals. The market for this advanced truck detection device includes public transportation agencies at the city, county, state, and national levels, traffic sensing device manufacturers, transportation consulting companies, and freight terminal operators. While current sensors may distinguish trucks from cars or trucks by axle configuration, there are no non-pavement intrusive technologies currently able to predict the body-type of the vehicle in enough detail to indicate freight carried. Agencies tasked with data collection often must rely on time-consuming periodic surveys to estimate where and what freight is moving on their highway system, making it difficult to produce timely project cost-benefit and resilience/impact analyses. Commercial applications can be extended to large distribution centers, mining areas, rail yards or other intermodal terminals and ports.This I-Corps project will further develop a system for low-cost, anonymous, and pavement-nonintrusive advanced truck detection by developing a side-fire (perpendicular to traffic flow) Lidar (Light Detection and Ranging)-based traffic detection and classification system. In side-fire configuration, Lidar sensors capture the profile of the truck (tractor and trailer/semi-trailer) which can be classified by body type with high-resolution while maintaining the anonymity of the driver, license/registration, and company. The innovation of this technology includes: 1) novel configuration and application of off-the-shelf Lidar technology for traffic detection, 2) coupling of technology with machine learning algorithms for feature detection, extraction, and classification with the aim of high-resolution truck classification, and 3) implementation of classification outputs in a data dashboard for real time and historical review. This novel truck detection solution using Lidar can enable a fundamental shift in how freight data is collected, especially by public transportation agencies.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.