详情

Vehicle Sensor Data (VSD) Based Traffic Control in Connected Automated Vehicle (CAV) Environment

项目名称：	Vehicle Sensor Data (VSD) Based Traffic Control in Connected Automated Vehicle (CAV) Environment
摘要：	Connected Automated Vehicle (CAV) are typically equipped with communication devices (e.g., DSRC) and on-board sensors (e.g., Radar, Lidar, Camera, etc.). Communication devices would enable exchange of real-time information between vehicles and infrastructures via Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) channels. Sensors equipped in vehicles are providing various Vehicle Sensor Data (VSD) such as the CAV�s global positioning system (GPS) location, speed and moving direction, and traffic data of detected regular vehicles. In current applications, collected VSD are commonly used for avoiding collisions only. Therefore, most existing CAV-based traffic control models, which only rely on the trajectory data of CAVs, would fall short of efficiency when CAV penetration rate is low. In this project, the research team aims to fully utilize VSD for traffic operation purpose and develop an innovative traffic control framework that can facilitate the implementation of CAV-based systems. Using on-board sensors, the system can monitor traffic conditions surrounding each CAV. Then through both V2V and V2I channels, all collected information will be integrated and a dynamic Ad-Hoc Sensor Network (ADSN) will be constructed. Under such framework, each CAV can be treated as a moving traffic sensor and detected regular vehicles will be �linked� with the CAVs. The objectives of this project are summarized as follows: 1) develop a macroscopic traffic flow model to estimate freeway traffic state information using VSD; 2) perform real-time speed control of CAVs for traffic efficiency improvement; and 3) prove the effectiveness of the proposed model under low CAV penetration rate condition with experimental tests. This project will demonstrate a proof of low CAV rate control concept which can serve as the foundation of future studies.
状态：	Completed
资金：	40000
资助组织：	University of Utah, Salt Lake City
管理组织：	TREC at Portland State University
项目负责人：	Hagedorn, Hau
执行机构：	University of Utah, Salt Lake City
主要研究人员：	Yang, Xianfeng
开始时间：	20171101
预计完成日期：	20181031
实际结束时间：	20181219