Enhancing driver situational awareness through crowd intelligence

Abstract

Providing up-to-date and reliable dynamic traffic information has become a major challenge in Intelligent Transportation Systems (ITS). Current traffic information services employ participatory sensing and crowd sourcing techniques to provide dynamic traffic information consisting of estimated travel times and congestion warnings. In this paper we propose the crowd sensing system based on smartphones and integrated GPS and accelerometer sensors, to capture dynamic, short-lasting traffic events and drivers’ aggressive and risky behavior like excessive breaking, sudden lane change, open turns, speed excess and route deviations. These traffic events can be categorized as risky driving situations or near accidents and are typically not included in any official traffic statistics. The system uses crowd intelligence based on participatory sensing paradigm and collaborative mechanisms to detect important traffic events/situations. It provides proactive notification and recommendations to drivers in the vicinity of, or on the route to reported events. The proposed crowd sensing system ensures information reliability through space–time clustering of reported events/situations while preserving drivers’ privacy. We perform the evaluation of our system that demonstrates the effectiveness and usefulness of advanced navigation service based on it, that uses collected dynamic traffic events to provide warning and notifications to drivers and safest route navigation. This novel functionality of a navigation service enhances drivers’ situational awareness, increasing safety and effectiveness of the traffic.