I-Corps: Translation Potential of an Autonomous Road Assessment System

I-Corps项目的更广泛影响是开发道路评估和维护技术平台。目前，需要更频繁和准确的道路状况评估。这种评估传统上受到劳动密集、不频繁和主观的人工检查的阻碍。该技术利用一组配备摄像头、深度传感器、全球定位系统（GPS）和加速度计的移动传感代理，实现道路状况评估和维护方式的现代化。其目的是摆脱传统的道路评估方法，提供一个自主、连续和客观的系统。这种新的评估技术有可能节省数十亿美元的道路维护成本，而且还可以通过更快地对新出现的道路问题作出反应来显著提高公共安全。这个I-Corps项目利用体验式学习和对行业生态系统的第一手调查来评估该技术的翻译潜力。该解决方案基于综合路面评估系统的开发，该系统利用自主传感、机器学习和大规模数据解释来对道路进行智能状况评估。该技术利用低成本移动传感代理网络，收集路面状况的实时、高分辨率数据，并通过人工智能（AI）算法进行分析，得出道路健康状况评估。该技术融合了多个领域，包括用于部署大量传感器的物联网（IoT），用于解释收集的大量数据集的人工智能，以及用于增强数据收集过程的众包。这种方法可以从基于计划的维护转变为基于状况的维护，从而更快地对道路磨损和潜在问题做出反应，从而增强公共安全，并可能节省数十亿美元的道路维护成本。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。

The broader impact of this I-Corps project is the development of a road assessment and maintenance technology platform. Currently, there is a need for more frequent and accurate road condition evaluations. Such assessments have traditionally been hindered by labor-intensive, infrequent, and subjective manual inspections. This technology utilizes a fleet of mobile sensing agents equipped with cameras, depth sensors, a global positioning System (GPS), and accelerometers to modernize the way road conditions are assessed and maintained. The aim is to move away from traditional road assessment methods and provide a system that is autonomous, continuous, and objective. This new assessment technology has the potential to save billions in road maintenance costs but also may significantly enhance public safety by enabling quicker responses to emerging road issues.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of an integrated pavement assessment system that utilizes autonomous sensing, machine learning, and large-scale data interpretation for intelligent condition assessment of roadways. By leveraging a network of low-cost mobile sensing agents, the technology collects real-time, high-resolution data on pavement conditions, that, when analyzed through artificial intelligence (AI) algorithms, yields an assessment of roadway health. The technology merges several fields including the Internet of Things (IoT) for deploying the swarm of sensors, AI for interpreting the massive data sets collected, and crowdsourcing to augment the data gathering process. This approach may allow for a shift from schedule-based maintenance to condition-based maintenance, enabling quicker responses to road wear and potential issues, thus enhancing public safety and potentially saving billions in road maintenance costs.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.