MUDVA: A multi-sensory dataset for the vehicular CPS applications

Vehicular Cyber-Physical System (VCPS) is a new trend in the research of the intelligent transport systems (ITS). In VCPS, vehicles work as a hub of sensors to collect interior and exterior information about the vehicle. Vehicles can use ad-hoc networking or 3G/LTE communication technology to share useful information with their neighboring vehicles or with the infrastructures to accomplish user safety, comfort, and entertainment tasks. In order to facilitate efficient sensor-services fusion in the VCPS applications, we need real life vehicular sensory datasets. While there has been many datasets containing vehicle mobility traces, there is hardly any that contains sensory information to be shared on the network. In this paper, we present a scenario specific modular dataset architecture along with some multi-sensory dataset modules. One of the dataset modules provides time synchronized multi-vehicle data including multi-view video, multi-directional sound, GPS, accelerometer, gyroscope, and magnetic field sensors. Each of the three vehicles recorded front, back, left, and right videos while moving closely in the suburban areas to let explore vehicular cooperative applications. Another module presents necessary tools and datasets to identify vehicular events such as acceleration, deceleration, turn, and no-turn events. We also present development details of a safety application using the presented datasets along with a list of other possible applications.