Abstract
The rapid development of road networks in highly urbanized cities requires a substantial number of viaducts to reduce the increasing traffic burden on urban highways. Unfortunately, this road design deteriorates the performance of global positioning system (GPS) navigators due to the signal blockage between satellite and receiver. As a result, it is difficult for GPS navigators to determine whether the vehicle is driving on the street or viaduct. Misleading information could confuse drivers and lead them to drive irregularly, which is dangerous in heavy traffic. This paper proposes a novel classification algorithm based on the fact that different satellite signals and conditions can be observed in the on-street and on-viaduct cases by the implementation of dynamic Bayesian network (DBN) to distinguish the driving area of a vehicle. In addition, the proposed DBN can also accurately estimate the driving altitude of the vehicle according to the experiment results.
Original language | English |
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Article number | 8003480 |
Pages (from-to) | 175-184 |
Number of pages | 10 |
Journal | IEEE Transactions on Intelligent Vehicles |
Volume | 2 |
Issue number | 3 |
DOIs | |
Publication status | Published - Sept 2017 |
Keywords
- Autonomous driving
- GNSS
- GPS
- land application
- localization
- navigation
- urban canyon
ASJC Scopus subject areas
- Artificial Intelligence
- Automotive Engineering
- Control and Optimization