This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Hybrid Navigation System That Combines Cloud and On-Board Computing
Technical Paper
2018-01-0022
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
A hybrid navigation system [1] that performs route calculations and highly flexible natural speech location searches in the cloud using dynamic databases that combine probe data collected from the vehicle and external data, and transmits to on-board devices has been developed. The system automatically switches to the on-board device when the vehicle is out of mobile network communication range or when faster processing is required for tasks such as re-routing.
The transition between the on-board devices and the cloud provide a seamless user experience adapted to use conditions and other factors. In addition, representing the route downloaded from the cloud by the on-board device requires synchronizing the map with the cloud, and a map caching function has been used to reduce the volume of data that needs to be synchronized.
The cloud-based route calculation is based not only on average travel time, but on dispersion as well. Moreover, integrating entry and exit link direction data (straight line, left turn, or right turn) enables the system to present routes involving less driving time and more precise estimates of arrival time.
This paper describes the technological attributes of the above hybrid navigation system.
Recommended Content
Technical Paper | High Speed Raw Radar Data Acquisition using MIPI CSI2 Interface for Deep Learning in Autonomous Driving Applications |
Book | LiDAR Technologies and Systems |
Authors
Citation
JIN, X., Nakamura, M., TETSUO, S., and Sugimoto, H., "Hybrid Navigation System That Combines Cloud and On-Board Computing," SAE Technical Paper 2018-01-0022, 2018, https://doi.org/10.4271/2018-01-0022.Also In
References
- Toyota Global Newsroom Toyota Develops New Hybrid Navigation and Voice Recognition Functions as Part of Toyota’s “Connected Strategy” http://newsroom.toyota.co.jp/en/detail/18480400
- Shen , J. and Ban , Y. Route Choice of the Shortest Travel Time Based on Floating Car Data Journal of Sensors 2016 7041653:1 7041653:11 2016 10.1155/2016/7041653
- Rahmani , M. , Jenelius , E. , and Koutsopoulos , H. Route Travel Time Estimation Using Low-Frequency Floating Car Data Proceedings of the IEEE Conference on Intelligent Transportation Systems the Hague 6-9 Oct 2013 10.1109/ITSC.2013.6728569
- Zhang , D. , Chow , C.Y. , Li , Q. , and Liu , A. Efficient Evaluation of Shortest Travel-time Path Queries in Road Networks by Optimizing Waypoints in Route Requests through Spatial Mashups presented at APWeb 2016: Web Technologies and Applications China 23-25 Sept 2016
- Dijkstra , E.W. A Note on Two Problems in Connexion with Graphs Numerische Mathematik 1 1 269 271 1959 10.1007/BF01386390
- Natori , M. Mann-Whitney U Test and Two-Sample Tests to Compare Measures of Central Tendency in the Case of Unequal Variances Primate Research 30 1 173 185 2014 10.2354/psj.30.006
- Kasai , M. and Uchiyama , H. A Study on Estimation of Probabilistic Changing Travel Time Based on Bayesian Statistics presented at 12th WCTR Portugal 11-15 July 2010
- Arima , K. , Ando , N. , Taniguchi , E. , and Yamada , T. A Route Choice Model Considering Mean Travel Time and Its Variance on Road Networks Infrastructure Planning Review 27 4 779 785 2009
- Uesugi , Y. , Iryo , T. , Oneyama , H. , Horiguchi , R. et al. Estimation of Expectation and Variance for a Section Travel Time Based on Fragmentary Probe Trajectories Infrastructure Planning Review 20 923 929 2003 10.2208/journalip.20.923
- Morikawa , T. , Yamamoto , T. , Miwa , T. , and Ohritsu , A. Development and Performance Evaluation of Dynamic Route Guidance System “PRONAVI” Traffic Engineering 42 3 65 75 2007
- Uesaka , K. , Sekiya , H. , Hashimoto , H. , Harada , Y. , et al. 2011
- Ota , K. , Oshige , S. , Yabe , T. , Imai , R. et al. 2013