关键词: 自动驾驶；局部路径规划；交叉路口；拓扑地图；计算机视觉

Abstract: Intersection is a complex traffic scene in the development of autonomous driving. The cost of using high-definition map scheme is high, and it is difficult to effectively identify intersection shape only by vehicle sensors. Therefore, an intersection local path planning algorithm based on open source topology map and vision is proposed. First, uses the open source topology map to plan the global navigation path which was used as a guide line. Then, semantic segmentation technology is used to identify the freespace in the visual image. At the same time, the local path’s starting point, end point and control points are determined by combining the real-time positioning information of the vehicle at the intersection. Finally, bazel curve interpolation method is used to obtain the curve cluster of alternative paths, and the optimal local path is selected according to the result of the multi-dimensional weighted cost function. Then realizes the autonomous driving of the vehicle in the process of turning at the intersection. Experimental results show that the proposed strategy can effectively plan local paths at intersections without relying on high precision maps and improve the ability of autonomous vehicle to pass at intersections. The passing rate at intersection can reach 99%.

Key words: Autonomous vehicles; Local path planning; Intersection; Topology map; Computer vision