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

Abstract

From today's conventional cars to tomorrow's selfdriving 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 (SVN), 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 like 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.

Services and simulation frameworks for vehicular cloud computing: a contemporary survey

“…Integration of social media platforms with vehicular clouds is an interesting area of research. Kwak et al [43] presented a social vehicle navigation system whereby users share the geo-tagged traffic images, videos, or messages referred as Navigation Tweets (NaviTweets). After processing, the system builds an online visual traffic information system referred as traffic digest.…”

Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges

“…However, the aforementioned technologies are not yet capable of supporting a gigabit per second data rate for sharing of onboard raw sensor data (i.e., from visual cameras, radars and LiDARs) between the vehicles and with the infrastructure [3]. Automotive cameras are typically responsible for generating a considerable proportion of sensor data on the vehicles, and the required data rates are typically around 100 Mbps and 700 Mbps for low-and high-resolution raw images, respectively, after significant compression has been applied [14]. Practically, the maximum data rate for DSRC is only around 6-27 Mbps, while 4G cellular systems are still limited to approximately 100 Mbps in high mobility scenarios, though much lower data rates are typical.…”

A low‐complexity architecture using diversity techniques for Internet of Vehicles with enhanced radio‐frequency performance

“…Another example is data transmissions for vehicles, passengers, or pedestrians positioned in vehicular blind spots. In this scenario, users are unable to directly identify the existence of other vehicles, so an urgent distance alert is exchanged using the IoV …”

“…In this scenario, users are unable to directly identify the existence of other vehicles, so an urgent distance alert is exchanged using the IoV. 2 IoV communications have attracted widespread research interest due to the challenges inherent in vehicular channels, such as the high mobility of vehicles, wide range of relative speeds between nodes, and the multitude of systems and applications. 3 These research issues point to the importance of radio-frequency (RF) performance in vehicular applications.…”