Total views : 459
A Design of Road Database for Self-Driving Vehicles
In recent industry, self-driving vehicles are very actively researched because they are considered as a new industrial power in most countries. In self-driving vehicles, many different fields should be researched at the same time. The research of road database for self-driving vehicles should not be indispensable in those fields. In order to design the road database for self-driving vehicles, the conventional road database should be enhanced in many sides, because it is designed as auxiliary equipment under assumption that a human is driving the vehicle. While the conventional road database are focused on the accumulation of static data, the road database for self-driving vehicles must include dynamic data such as roads that may be temporarily closed because of repairing. This paper proposes the design of a road database for self-driving vehicle so that it includes dynamic data as well as static data on roads. In order to provide a generality and scalability of road database, we design the database by using the well-known Entity-Relationship Model. In order to simplify the database, we also use the abstracting method and then extracted 6 entities and 10 relationships. From these entities and relationship, the entity-relationship diagram is also proposed.
Database, Entity-Relationship Model, Road, Self-Driving Vehicle.
- Guizzo E. How google’s self-driving car works. IEEE Spectrum Online. 2011 Oct 18.
- Wei L, Soheilian B, Gouet-Brunet V. Augmenting vehicle localization accuracy with cameras and 3D road infrastructure database. In Computer Vision-ECCV Workshops. 2014; 194–208.
- Agarwal PK, Verma DV, Mehar R. Need for an effective accident data system in India to improve road safety. Journal of Advanced Research in Automotive Technology and Transportation System. 2014; 1(1/2):26–36.
- Ding D, Yoo J, Jekyo J, Jin S, Kwon S. Efficient roadsign detection based on machine learning. Bulletin of Networking, Computing, Systems and Software. 2015; 4(1):15–17.
- Budhathoki NR, Haythornthwaite C. Motivation for open collaboration crowd and community models and the case of OpenStreetMap. American Behavioral Scientist. 2013; 57(5):548–75.
- The National Leader in Policy and Professional Development for the Transportation Industry [Internet]. Available from: https://www.enotrans.org.
- Hu Y, Gong J, Jiang Y, Liu L, Xiong G, Chen H. Hybrid map based navigation method for unmanned ground vehicle in urban Scenario. Remote Sensing. 2013; 5(8):3662–80.
- Jo K, Sunwoo M. Generation of a precise roadway map for autonomous cars. IEEE Transactions on Intelligent Transportation Systems. 2014; 15(3):925–37.
- Gruyer D, Belaroussi R, Revilloud M. Accurate lateral positioning from map data and road marking detection. Expert Systems with Applications. 2016; 43:1–8.
- Fendi KG, Adam SM, Kokkas N, Smith M. An approach to produce a GIS database for road surface monitoring. APCBEE Procedia. 2014; 9:235–40.
- Chen P. The entity-relationship model-toward a unified view of data. ACM Transactions on Database Systems. 1976; 1(1):9–36.
- Tseng FH, Liang TT, Lee CH, Der Chou L, Chao HC. A star search algorithm for civil UAV path planning with 3G communication. In Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). 2014:942–45.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.