I-Corps: Curve safety assessment solution to help transportation agencies with road curve safety management

这个i-Corps项目的更广泛的影响/商业潜力是开发一种经济高效的解决方案，以改善交通网络的道路弯道安全，特别是弯道安全评估和弯道警告标志的合规性检查。提出的解决方案可能有助于交通运输机构将道路安全管理实践从被动转变为主动，以拯救生命，提高整体道路安全。拟议的技术可能有助于为交通机构节省时间和精力，还可以促进交通安全公平，特别是对于资源有限、无法改善道路安全的地方机构和代表性不足的社区。拟议的技术可能会提供一种更具成本效益和效率的替代方案，增强最先进的做法，以帮助机构满足联邦法规并避免道路碰撞的潜在责任。这个i-Corps项目是基于提高交通网络道路曲线安全的技术开发的。这些技术使用移动和物联网数据、计算机视觉和机器学习以及先进的计算模型来执行曲线安全评估、清点和监测曲线警告标志，并评估是否符合联邦法规。该技术使用机构内部的众包和先进的计算模型来提取曲线几何性质和确定咨询速度。该解决方案还使用全球定位系统(GPS)和图像数据，结合计算机视觉和深度学习方法，进行自动曲线警告标志检测和合规性分析。以前的结果表明，这些技术有潜力显著提高安全管理实践的效率和成本效益，方法是实现对弯道安全的例行评估，定期清点现有的弯道警告标志，并进行合规性分析，以确定缺陷并支持安全改进计划。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 the development of a cost-effective solution to improve roadway curve safety of transportation networks, particularly curve safety assessment and compliance checking of curve warning signage. The proposed solution may help transportation agencies transform the management practices of roadway safety from reactive to proactive to save lives and improve overall roadway safety. The proposed technology may help save time and effort for transportation agencies, and also promote transportation safety equity, especially for local agencies and underrepresented communities that have limited resources to improve their roadway safety. The proposed technology may offer a more cost-effective and efficient alternative that enhances state-of-art practices to help agencies meet federal regulations and avoid potential liabilities from roadway collisions. This I-Corps project is based on the development of technologies to improve the roadway curve safety of transportation networks. The technologies use mobile and Internet of Things data, computer vision and machine learning, and advanced computation models to perform curve safety assessments, inventory and monitor curve warning signage, and evaluate compliance with federal regulations. The proposed technology uses intra-agency crowdsourcing and advanced computation models for curve geometric property extraction and advisory speed determination. The solution also uses global positioning systems (GPS) and imagery data combined with computer vision and deep learning methods for automated curve warning sign detection and compliance analysis. Previous results show the potential for these technologies to significantly improve the efficiency and cost-effectiveness of safety management practices by enabling routine assessment of curve safety, routinely inventorying existing curve warning signage, and conducting compliance analyses to identify deficiencies and support safety improvement planning.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.