Abstract

There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Automated Driving (AD) features. These range from a need for road determination (<5 meters), lane determination (<1.5 meters), and determining where the vehicle is within the lane (<0.3 meters). This work examines the performance of Global Navigation Satellite Systems (GNSS) on 30,000 km of North American highways to better understand the automotive positioning needs it meets today and what might be possible in the near future with wide area GNSS correction services and multi-frequency receivers. This includes data from a representative automotive production GNSS used primarily for turn-by-turn navigation as well as an Inertial Navigation System which couples two survey grade GNSS receivers with a tactical grade Inertial Measurement Unit (IMU) to act as ground truth. The latter utilized networked Real-Time Kinematic (RTK) GNSS corrections delivered over a cellular modem in real-time. We assess on-road GNSS accuracy, availability, and continuity. Availability and continuity are broken down in terms of satellite visibility, satellite geometry, position type (RTK fixed, RTK float, or standard positioning), and RTK correction latency over the network. Results show that current automotive solutions are best suited to meet road determination requirements at 98% availability but are less suitable for lane determination at 57%. Multi-frequency receivers with RTK corrections were found more capable with road determination at 99.5%, lane determination at 98%, and highway-level lane departure protection at 91%.

Comment: Accepted for the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019


Original document

The different versions of the original document can be found in:

https://arxiv.org/pdf/1906.08180,
https://www.ion.org/publications/abstract.cfm?articleID=16914,
https://arxiv.org/abs/1906.08180,
https://ui.adsabs.harvard.edu/abs/2019arXiv190608180R/abstract,
https://au.arxiv.org/abs/1906.08180,
https://academic.microsoft.com/#/detail/2949837790
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Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.33012/2019.16914
Licence: CC BY-NC-SA license

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