PFI-TT: Smart City Curbside Parking Management

PFI-TT：智慧城市路边停车管理

• 批准号: 2313785
• 负责人: Wei Cheng
• 金额: $ 25万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313785&HistoricalAwards=false
• 关键词: PFI; TT; Smart; City; Curbside

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.

通过增强对智能移动性的科学和技术理解，这种伙伴关系对创新 - 技术翻译（PFI -TT）项目的更广泛的影响/商业潜力。这些城市将受益于准确的路缘侧停车管理系统，这可以帮助他们优化曲线车道空间，停车计划和停车定价。 Curveside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花费在寻找停车位的时间。通过停车搜索导航解决方案，驾驶员将在曲线停车搜索上花费更少的时间，避免使用非法停车，节省汽油并在停车场搜索期间享受安心。这些城市将从交通拥堵和污染减少中进一步受益。拟议的研究将促进新的研究和教育机会。学生将接受培训以进行机器学习（ML）和智能移动性的物联网研究。该项目是由主要由女性研究人员，女性企业家和少数民族组成的团队进行的。拟议的项目将通过共同利用ML和IoT技术来建立智能曲线停车管理系统。拟议的应用研究的目标是开发技术，以正确阅读和找到仪表板拍摄的街道视频的曲线停车标志，并从路边部署的相机中检测和跟踪停放的车辆和开放停车位。拟议的研究将解决（i）通过微调团队的AI模型以进行签名检测，标志跟踪和签名文本识别的方式，以（i）准确阅读在不同城市拍摄的街道视频中的曲线停车标志的技术障碍； （ii）通过融合视频探视技术，地图和视频GPS素材的结果/数据，可以准确地找到街道视频中的曲线停车标志； （iii）通过共同利用团队的路边停车清单数据库和AI模型来检测开放停车位，以进行车道检测和车辆检测； （iv）通过研究基于ML的停放车辆检测和跟踪技术来跟踪曲线侧停车位。预期的技术结果包括一系列的AI模型和算法，一个路边停车规则数据库以及用于路边停车库存管理的系统。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。