SBIR Phase I: WeighCam: A Non-invasive Screening Tool to Detect Overweight Trucks on Roads in Real Time

SBIR 第一阶段：WeighCam：一种非侵入式筛查工具，可实时检测道路上的超重卡车

• 批准号: 1913471
• 负责人: Shervin Taghavi Larigani
• 金额: $ 22.5万
• 依托单位: STL SCIENTIFIC LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913471&HistoricalAwards=false
• 关键词: SBIR; Phase; WeighCam; Non; invasive

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to decrease the number of overweight trucks on the roads by developing a financially and technically practical screening tool to detect overweight vehicles on most roads in real-time. Overweight commercial vehicles are a significant cause of the nation's crumbling roads and bridges besides being a prime threat to transportation safety.The proposed project addresses the issue of effectively detecting overweight trucks on roads using real-time video-images while neither interfering with the traffic flow nor integrity the pavement. The proprietary system, WeighCam system, includes proprietary electro-optical system, algorithms, and system design. It will be easy to operate and robust in the sense that it will reliably work in most ambient environmental conditions such as ambient luminosity changes, etc. The purpose of this research will be to validate the basics of the system and calibrate the measurements using known weights to deduce the system limitations and to establish the feature sets of the Minimum Viable Product (MVP), a necessary step to start the manufacture of a commercial prototype. The final prototype will be a practical, autonomous, quick to install, and continuously operating continuous system sending data in real time.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.

这项小企业创新研究（SBIR）第一阶段项目的广泛影响是，通过开发一种经济上和技术上实用的筛选工具，实时检测大多数道路上的超重车辆，从而减少道路上超重卡车的数量。超重的商用车辆是造成美国道路和桥梁破损的一个重要原因，也是交通安全的主要威胁。拟议中的项目解决了利用实时视频图像有效检测道路上超重卡车的问题，同时又不会干扰交通流量，也不会破坏路面的完整性。该专有系统，WeighCam系统，包括专有的光电系统、算法和系统设计。它易于操作，在大多数环境条件下（如环境亮度变化等）都能可靠地工作。本研究的目的是验证系统的基础知识，并使用已知的权重校准测量结果，以推断系统限制，并建立最小可行产品（MVP）的特征集，这是开始制造商业原型的必要步骤。最终的原型将是一个实用的、自主的、快速安装的、连续运行的、实时发送数据的连续系统。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。