PFI-TT: Broadening Real-Time Continuous Traffic Analysis on the Roadside using AI-Powered Smart Cameras

该创新技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力将允许先进人工智能（AI）和计算机视觉技术的普及，为交通系统工程师和当地交通部门提供方便和高效的交通分析。城市面临着各种与交通相关的问题，这些问题需要高质量的数据来解决，比如拥堵、事故和道路超载。通过本研究开发的智能路边摄像头设备，交通运营商可以以更低的成本获取实时车辆轨迹数据，进行交通研究或及时决策，提高道路安全和交通效率。此外，21世纪是信息时代，“数据是数字繁荣的新石油”。从这项拟议的研究中产生的技术可能会产生一系列有利于国家经济的道路交通新应用，例如更便宜的保险与细粒度的驾驶行为分析，更安全的街道与路边摄像头的事故警报，以及更好地适应未来的自动驾驶车辆通过该技术获得的数据的见解。此外，这项研究将扩大主要本科机构和西班牙裔服务机构的研究参与，并培养未来的创新和创业领袖。拟议的项目侧重于新技术，使交通研究人员能够收集现有道路传感器系统无法提供的细粒度车辆轨迹数据，实时分析数据，检测和分类不同的车辆类型，估计车辆速度和轨迹，并预测潜在的碰撞和事故。具体而言，该项目将开发三项技术：第一，利用车辆关键点和人工智能（AI），开发一种新颖的基于3D车辆模型的解决方案，实现准确的车辆跟踪和定位；第二，通过AI模型压缩和图形处理单元（GPU）上的高效并行计算，加速部署在路边的低功耗边缘设备上的AI实时性能的方法；第三，利用一种新型的相机和多视图来提高黄昏机器视觉的鲁棒性。为了展示这些新技术的可用性，将开发一套道路交通数据分析应用程序，包括实时在线交通可视化、细粒度交通计数、定量指标的道路安全分析和交通事件检测。该项目的成果将在路边智能摄像头设备中实现，该设备可以在低光照条件下以分米级的精度、实时效率和鲁棒性在3D空间中跟踪和定位车辆。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/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project will allow democratization of advanced Artificial Intelligence (AI) and computer vision technologies to enable convenient and efficient traffic analysis for transportation system engineers and local departments of transportation. Cities face various traffic-related problems that need high-quality data to solve, such as congestion, accidents, and road overload. With the smart roadside camera device developed from this research, traffic operators can obtain real-time vehicle trajectory data at a much lower cost and conduct traffic studies or make prompt decisions to improve road safety and transportation efficiency. Moreover, the 21st century is the era of information, and "data is the new oil" of digital prosperity. The technologies generated from this proposed research can potentially spawn a set of new applications for road transportation that benefit the nation's economy, such as cheaper insurance with a fine-grained driving behavior analysis, safer streets with accident alerts from roadside cameras, and better accommodation of future automated vehicles with the insights from the data obtained through this technology. Additionally, this research will broaden the research participation in a primarily undergraduate institution and a Hispanic Serving Institution, as well as train future leaders in innovation and entrepreneurship.The proposed project focuses on novel technologies that allow traffic researchers to collect fine-grained vehicle trajectory data that existing road sensor systems cannot provide, analyze data in real-time, detect and classify different vehicle types, estimate vehicle speeds and trajectories, and predict potential collisions and accidents. Specifically, the project will develop three technologies: First, a novel 3D vehicle model-based solution for accurate vehicle tracking and localization using vehicle key point and Artificial Intelligence (AI); Second, a method to accelerate AI on low-power edge devices deployed on the roadside for real-time performance through AI model compression and efficient parallel computing on the graphical processing unit (GPU); Third, robustness improvement of machine vision in twilight by leveraging a novel type of camera and multiple views. To demonstrate the usability of these novel technologies, a set of road traffic data analysis applications will be developed, including real-time online traffic visualization, fine-grained traffic counting, road safety analysis with quantitative metrics, and traffic incident detection. The outcome of this project will be realized in a roadside smart camera device that can track and localize vehicles in the 3D space with decimeter-level accuracy, real-time efficiency, and robustness under low lighting conditions.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.