The Research on Establishment of Database for Autonomous-driving in Downtown

The waypoint tracking method of many methods is common. The raw-data included information(road shapes, speed bumps, signal lights, stop lines, etc) to need when do autonomous driving in downtown suggest a available method and a effective data-management method. The existing waypoint tracking method is greater reliance on raw-data. That's because of the only latitude and longitude data. This is not flexible in using data, so with INS, the reduction zone of speed and stop live such as speed bumps, intersections, turnings, crosswalks compared to stopping zone like railroad, these data is stored with waypoint data. Data made by this way becomes essential information to speed planning by road shape and activation time of suitable sensor in necessity. Additionally, the waypoints with latitudes and longitudes information include various road information. The real road of in downtown is stored by segment through polynomial fitting. If certain format is stored about all the roads, not only efficient data management, speed planning of road shape and environment perception but also avoidance path planning of multilane road and path planning of in downtown such as lane change based on necessary information can be realized and it can be possible to drive more efficiently.