Road and obstacle detection based on multi-layer laser radar in driverless car

To make a driverless car with better environment awareness, multi-layer laser radar was applied to detect roads and obstacles. Firstly the road edge data set was extracted from numerous laser radar data based on characteristics of the road edge data, and the cluster analysis of the data sets was done with the improved COBWEB algorithm based on Euclidean distance. In order to divide the road into drivable area and undrivable area, the left and right road edges were respectively fitted into a straight line with the least squares method. Secondly DSmT was applied to establish a grid map for the environment, and dynamic obstacles were detected by the conflict coefficient within drivable area. Finally, the cluster analysis and information extraction of dynamic obstacles was completed by the expansion algorithm, erosion algorithm and improved eight neighborhood labeling algorithm. The results show that the algorithm can significantly reduce redundant operations and improve efficiency.