Seeing Is Believing: Sharing Real-Time Visual Traffic Information via Vehicular Clouds

Daehan Kwak, Ruilin Liu, Daeyoung Kim, Badri Nath, Liviu Iftode

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


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 information, people and vehicles are sharing sensing data to enhance the driving experience. This paper describes a vehicular cloud service for route planning, where users collaborate to share traffic images by using their vehicles' on-board cameras. We present the architecture of a collaborative traffic image-sharing system called social vehicle navigation, which allows drivers in the vehicular cloud to report and share visual traffic information called NaviTweets. A set of NaviTweets 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, such as estimated time of arrival, thereby supporting users' route decision making. As proof of concept, this paper presents the system design and a prototype implementation running on the Android smartphone platform, along with its evaluation.

Original languageEnglish
Article number7470581
Pages (from-to)3617-3631
Number of pages15
JournalIEEE Access
StatePublished - 2016


  • Crowdsourcing
  • Vehicular networks
  • navigation systems
  • route choice/planning
  • social networks
  • social sensors
  • traffic images
  • vehicular clouds


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