A Framework for Adaptive Resolution Geo-Referencing in Intelligent Vehicular Services

Abstract

Future smart cities are profoundly looking forward to providing services that assure daily competent functionality. Efficient traffic management and related vehicular services are crucial aspects when considering the city’s decent operation. The significant presence of the vehicular and smartphone sensing and computing capabilities within and amongst the vehicles open the door towards robust vehicular and road services. The retrofitted present and future vehicles will be able to provide accurate real-time information about the road conditions and hazards, driver behaviour, and traffic. Adequate geo-referencing is remarkably demanded in order to preserve robustness while providing vehicular services. Present and widely spread global positioning systems (GPS) receivers are providing low- resolution position update at 1 Hz, which is not sufficient at high speeds. Also, alternative high data rate geo-referencing technologies may face self-contained or environmental-based performance limitations. In this paper, we propose an adaptive resolution integrated geo-referencing framework that augments GPS and inertial sensors to provide accurate localization and positioning for road information services. Also, we examine the effectiveness of the proposed system in geo- referencing for selected real-life road services.