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Autonomous Guided Vehicles (AGVs) or Autonomous Vehicles (AVs) are in widespread use in many applications such as bomb disposal, underwater exploration, industrial transport, etc. Control and guidance are vital aspects of AV research and many techniques have been proposed in literature which range from fully autonomous and intelligent systems to laser and radar guided systems and line followers. This paper presents a new technique for guiding and controlling autonomous vehicles by using the OpenStreetMap (OSM) map library. An AV with a built-in GPS receiver can be controlled effectively from a central computer which has the OSM powered control mechanism installed. The system proposed in this paper can control the AV in an online-mode or offline-mode depending upon the quality of the link between the AV and the central computer. A sample implementation of the system is also given. Automated guided vehicle (AGV) or Autonomous Vehicle (AV) is a driverless mobile robot that can be controlled and navigated across a surface (terrestrial AGV) or through some medium (air or water) remotely. Automated vehicles and machinery are an essential part of today's industry. In any industrial setup, most of the machinery is automated, particularly that related to production. Unmanned Aerial Vehicles (UAVs) are another common class of automated vehicles used for reconnaissance and attack by military forces. Terrestrial AGVs are still in evolution, although most of these vehicles are used by military these days for instance for bomb disposal, yet the notion that AGVs can replace many current transportation means is highly rated. Since the advent of Automation, the guidance and control of an AGV became the primary focus for research groups. Various algorithms have been developed for the guidance mechanism and different techniques have been implemented for the control of the autonomous vehicles. Some of the techniques use guide tape, laser (1) and even gyroscopic guidance. For instance, in a vision based lane detection of an autonomous vehicle (2), different algorithms were used to get the optimum guidance and navigation results. In another method (3), a path generation algorithm was developed using

Autonomous Vehicle Guidance and Control using OpenStreetMap and Advanced Integration Techniques