Total views : 459

A Design of Road Database for Self-Driving Vehicles


  • Department of Multimedia Engineering, Hansung University, Korea, Republic of


Background/Objectives: 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. Methods/Statistical Analysis: 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. Findings: 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. Improvements: The design road database can also be extended by using other relational methods than Entity-Relationship model.


Database, Entity-Relationship Model, Road, Self-Driving Vehicle.

Full Text:

 |  (PDF views: 247)


  • Guizzo E. How Google’s self-driving car works. IEEE Spectrum Online; 2011 Oct.
  • Wei L, Soheilian B, Gouet-Brunet V. Augmenting vehicle localization accuracy with cameras and 3D road infrastructure database. In Computer Vision-ECCV Workshops; 2014. p. 194–208.
  • Singh AP, Agarwal PK, Sharma A. Road safety improvement: A challenging issue on Indian. International Journal of Advanced Engineering Technology. 2011 Apr-Jun; 2(2):363–9.
  • Budhathoki NR, Haythornthwaite C. Motivation for open collaboration crowd and community models and the case of OpenStreetMap. American Behavioral Scientist. 2013 May; 57(5):548–75.
  • The eno center for transportation. Available from:
  • 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 Jul; 5(8):3662–80.
  • Jo K, Sunwoo M. Generation of a precise roadway map for autonomous cars. IEEE Transactions on Intelligent Transportation Systems. 2014 Jun; 15(3):925–37.
  • Gruyer D, Rachid B, Marc R. Accurate lateral positioning from map data and road marking detection. Expert Systems with Applications. 2016 Jan; 43:1–8.
  • Kamboj D, Kumar V, Vaid R. Secure and authenticated vehicle navigation system. Indian Journal of Science and Technology. 2015 Oct; 8(28):1–7.
  • Rizwan JM, Krishnan PN, Karthikeyan R, Kumar SR. Multi layer perception type artificial neural network based traffic control. Indian Journal of Science and Technology. 2016 Feb; 9(5):1–6.
  • Fendi KG, Adam SM, Kokkas N, Smith M. An approach to produce a GIS database for road surface monitoring. APCBEE Procedia. 2014 Dec; 9:235–40.
  • Chen P. The entity-relationship model - Toward a unified view of data. ACM Transactions on Database Systems. 1976 Mar; 1(1):9–36.
  • Rajakumari SB, Nalini C. An efficient data mining dataset preparation using aggregation in relational database. Indian Journal of Science and Technology. 2014 Jun; 7(S5):44–6.
  • Tseng FH, Liang TT, Lee CH, Der Chou L, Chao HC. A star search algorithm for civil UAV path planning with 3G communication. 2014 10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP); 2014 Aug. p. 942–5.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.