PFI-TT: Smart City Curbside Parking Management

这一创新-技术转化伙伴关系(PFI-TT)项目通过加强对智能移动的科学和技术理解，在造福城市和司机方面产生了更广泛的影响/商业潜力。城市将受益于准确的路边停车管理系统，该系统可以帮助城市优化限行车道空间、停车规划和停车定价。路边停车库存数据库还将允许地图和导航服务提供商减少车辆开车四处寻找停车位的时间。有了停车搜索导航解决方案，司机在路边停车搜索上花费的时间将更少，避免违停，节省汽油，在停车搜索中安心。这些城市将进一步受益于交通拥堵和污染的减少。拟议的研究将促进新的研究和教育机会。学生将接受培训，进行智能移动方面的机器学习(ML)和物联网研究。这个项目是由一个主要由女性研究人员、女性企业家和少数民族组成的团队承担的。拟议的项目将联合利用ML和物联网技术构建智能路边停车管理系统。拟议的应用研究的目标是开发技术，从仪表盘摄像头拍摄的街道视频中正确读取和定位路边停车标志，并从路边部署的摄像头检测和跟踪停放的车辆和开放的停车位。拟议的研究将解决以下技术障碍：(I)通过微调该团队的人工智能模型进行标志检测、标志跟踪和标志文本识别，从而准确地读取不同城市的街道视频中的路边停车标志；(Ii)通过融合视频里程计技术、地图和视频GPS视频的结果/数据，从街道视频中准确定位路边停车标志；(Iii)联合利用该团队的路边停车库存数据库和用于车道检测和车辆检测的AI模型，检测开放的停车点；以及(Iv)通过研究基于ML的停放车辆检测和跟踪技术，跟踪路边停车的占用情况。预期的技术成果包括一系列人工智能模型和算法、路边停车规则数据库和路边停车库存管理系统。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 Partnerships for Innovation - Technology Translation (PFI-TT) project in benefitting cities and drivers by enhancing scientific and technological understanding of smart mobility. The cities will benefit from accurate curbside parking management system, which can help them optimize curb lane space, parking planning, and parking pricing. The curbside parking inventory database will also allow map and navigation service providers to lessen the amount of time that vehicles spend driving around looking for parking spaces. With the parking search navigation solution, drivers will spend less time on curbside parking search, avoid illegal parking, save gas, and enjoy peace of mind during parking search. The cities will further benefit from reduced traffic congestion and pollution. The proposed research will foster new research and education opportunities. Students will be trained to conduct machine learning (ML) and IoT research in smart mobility. This project is being undertaken by a team consisting primarily of female researchers, female entrepreneurs, and minorities. The proposed project will build a smart curbside parking management system by jointly utilizing the ML and IoT technologies. The goals of the proposed applied research are to develop technologies to correctly read and locate the curbside parking signs from the street videos taken by dashcams and to detect and track the parked vehicles and open parking spaces from roadside deployed cameras. The proposed research will address the technical hurdles in (i) accurately reading curbside parking signs from the street videos taken in different cities by fine tuning the team’s AI models for sign detection, sign tracking, and sign text recognition; (ii) accurately locating the curbside parking signs from the street videos by fusing the results/data from video odometry technology, map, and video GPS footage; (iii) detecting open parking spots by jointly utilizing the team’s curbside parking inventory database and the AI models for lane detection and vehicle detection; and (iv) tracking curbside parking occupancy by studying ML-based parked vehicle detection and tracking technologies. The anticipated technical results include a series of AI models and algorithms, a curbside parking rule database, and a system for curbside parking inventory management.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.