I-Corps: Advanced Truck Detection with Lidar Technology

I-Corps：采用激光雷达技术的先进卡车检测

• 批准号: 2140306
• 负责人: Sarah Hernandez
• 金额: $ 5万
• 依托单位: University of Arkansas
• 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: 2021-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140306&HistoricalAwards=false
• 关键词: Corps; Advanced; Truck; Detection; Lidar

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.

该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。 对于成功收集运输数据集，尤其是在竞争性货运行业中，匿名至关重要。 传统方法，例如基于图像的检测，手机跟踪或其他视觉监控（车牌跟踪或徽标识别）可能会违反隐私注意事项，并阻碍广泛的货运数据收集。 此类数据收集对于旅行需求建模和预测以及对道路，桥梁和货运终端的基础设施计划，运营和维护所必需的。 该高级卡车检测设备的市场包括城市，县，州和国家级别的公共交通机构，交通传感设备制造商，交通咨询公司和货运终端运营商。 尽管当前的传感器可以通过轴配置将卡车与汽车或卡车区分开，但目前没有非途径侵入技术能够以足够的细节来预测车辆的车身类型，以表明携带货物。 负责数据收集的代理商通常必须依靠耗时的定期调查来估算其高速公路系统上的货物的何处和货物，这使得很难及时产生及时的项目成本效益以及弹性/影响力分析。 商业应用可以扩展到大型分销中心，采矿区，铁路场或其他模式码头和端口。本I-Corps项目将进一步开发一个用于低成本，匿名和路面非触觉的高级卡车检测系统，通过开发侧向火灾（垂直到交通流动流动流量）LIDAR（LIDAR）LIDAR（轻度检测和范围）的交通流量系统。 在侧射配置中，激光雷达传感器捕获了卡车的轮廓（拖拉机和拖车/半拖车），可以按身体类型对高分辨率进行分类，同时保持驾驶员的匿名性，许可/注册和公司的匿名性。 该技术的创新包括：1）新的配置和用于交通检测的现成的激光雷达技术的新型配置和应用，2）技术与机器学习算法耦合，用于特征检测，提取和分类，目的是高分辨率卡车分类，以及3）在数据仪表板中实施分类措施，以实现真实的时间和历史审查。这种使用LiDAR的新型卡车检测解决方案可以使收集货运数据的基本转变，尤其是公共交通机构。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优点和更广泛的影响来通过评估来支持的。