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

该I-Corps项目的更广泛的影响/商业潜力是开发一种具有成本效益的解决方案，以提高交通网络的道路曲线安全性，特别是曲线安全评估和曲线警告标志的合规性检查。所提出的解决方案可以帮助运输机构将道路安全的管理实践从被动转变为主动，以挽救生命并提高整体道路安全。这项技术可以帮助运输机构节省时间和精力，并促进运输安全公平，特别是对于资源有限的地方机构和代表性不足的社区，以改善其道路安全。 所提出的技术可以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项目的基础是开发技术，以提高运输网络的道路曲线安全性。这些技术使用移动的和物联网数据、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、库存和监控曲线警告标志，并评估是否符合联邦法规。所提出的技术使用机构内众包和先进的计算模型进行曲线几何属性提取和咨询速度确定。 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，用于自动化曲线警告标志检测和合规性分析。以前的结果表明，这些技术有可能通过对弯道安全性进行常规评估，对现有弯道警告标志进行常规清点，并进行合规性分析，以确定缺陷和支持安全改进计划。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的支持知识价值和更广泛的影响审查标准。

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.