Research‎ > ‎

Social Vehicle Navigation

From today’s conventional cars to tomorrow's self-driving cars, advances in technology will enable vehicles to be equipped with more and more sophisticated sensing devices, such as cameras. As vehicles gain the ability to act as mobile sensors that carry useful traffic data, people and vehicles are sharing sensing data to enhance the driving experience. This project presents the architecture of a collaborative traffic image–sharing system called Social Vehicle Navigation (SVN), which allows drivers to report and share visual traffic information. Such shared information is then filtered, refined, and condensed into a concise, user-friendly snapshot summary of the route of interest, called a Traffic Digest. These digests can provide more pertinent and reliable information about the road situation and can complement predictions like the estimated time of arrival (ETA), thereby supporting users’ route decision making. As proof of concept, a prototype application running on the Android smartphone platform is developed, along with its system design and evaluation.