NORA: Towards Large-Scale Vehicular Analytics for Driving Environment Monitoring/Assessment

Abstract

Research in vehicular analytics has explored two different approaches to infer properties about a vehicle's surroundings: (a) using sensors on smartphone devices to infer properties about surroundings, or (b) to use in-vehicle sensors. The latter approach was less studied. In this paper, we take a first step beyond research to understand, using a pilot study, how to design vehicular analytics at scale. Our pilot prototype NORA (Network Oriented Road Applications) contains novel algorithms to detect roadside phenomena (such as potholes, rough road, and slippery surfaces). These leverage multiple in-vehicle sensors to disambiguate these phenomena from other conflating factors. NORA reliably detects, on a cloud service, roadside events from multiple individual in-vehicle detections. To do this, it uses careful clustering techniques to assess the spatial scale of the event, and belief decay techniques to match event duration. It also employs aggressive fleetwide suppression of detections to minimize communication cost. Through a 50-vehicle deployment of NORA pilot over 9 months, it is shown that the results obtained from NORA in-vehicle detection methods match very well with ground truth measurements, and NORA cloud is effective at aggregating road events in an accurate and efficient fashion.