CAREER: Sensor Fusion for Multi-Modal Traffic Sensing

职业：用于多模式交通传感的传感器融合

• 批准号: 1149989
• 负责人: Jakob Eriksson
• 金额: $ 40万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149989&HistoricalAwards=false
• 关键词: CAREER; Sensor; Fusion; Multi; Modal

The objective of this project is to lay the foundation for comprehensive real-time monitoring of traffic conditions throughout our urban streets and highways. The end goal: a scalable, model-based sensor fusion system capable of merging data from a large number of heterogeneous and geographically diverse sensors, and maintaining a coherent view of sensed, and inferred, traffic conditions. A city-wide, detailed and accurate view of current traffic conditions enables improvements in a wide range of applications in transportation. A key to achieving this goal affordabily is to find ways to extract more information from existing sensor infrastructure. Through collaborations with the City of Chicago, NAVTEQ, GCM Travel and the Chicago Transit Agency, the PI has access to Chicago-area data from sensing modalities including underground magnetic loops as well as bus, city-vehicle and crowd-sourced GPS traces. Complementing these sources are opportunistic video feeds from security, red-light and other traffic cameras, as well as vehicle re-identification based on radio frequency transmitters. With increasing congestion, rising fuel prices, and the threat of global warming, the need for improving the efficiency of our transportation systems has never been greater. Comprehensive sensing and inference of traffic conditions will help travelers make better travel decisions and help traffic engineers identify and fix problems. This project combines research with public service and open-source software development. The PI is partnering with a local high-school to host traffic sensing research interns, and the results from this project are used in a graduate course on transportation sensing at UIC.

该项目的目标是为全面实时监测整个城市街道和高速公路的交通状况奠定基础。最终目标：一个可扩展的，基于模型的传感器融合系统，能够合并来自大量异构和地理上不同的传感器的数据，并保持一个连贯的观点，感测，推断，交通状况。对当前交通状况的全市范围、详细和准确的了解可以改善交通领域的广泛应用。实现这一目标的关键是找到从现有传感器基础设施中提取更多信息的方法。通过与芝加哥市、NAVTEQ、GCM Travel和芝加哥交通局的合作，PI可以从传感模式（包括地下磁环以及公共汽车、城市车辆和众包GPS轨迹）中获取芝加哥地区的数据。除了这些来源之外，还有安全、红灯和其他交通摄像头提供的随机视频，以及基于射频发射器的车辆重新识别。随着日益严重的交通堵塞、不断上涨的燃料价格以及全球变暖的威胁，提高交通系统效率的需求从未如此之大。对交通状况的全面感知和推断将帮助旅行者做出更好的出行决策，并帮助交通工程师识别和解决问题。该项目将研究与公共服务和开源软件开发相结合。PI正在与当地一所高中合作，举办交通传感研究实习生，该项目的成果将用于UIC的交通传感研究生课程。