  It is a crucial step to achieve effective semantic segmentation of lane
marking during the construction of the lane level high-precision map. In recent
years, many image semantic segmentation methods have been proposed. These
methods mainly focus on the image from camera, due to the limitation of the
sensor itself, the accurate three-dimensional spatial position of the lane
marking cannot be obtained, so the demand for the lane level high-precision map
construction cannot be met. This paper proposes a lane marking semantic
segmentation method based on LIDAR and camera fusion deep neural network.
Different from other methods, in order to obtain accurate position information
of the segmentation results, the semantic segmentation object of this paper is
a bird's eye view converted from a LIDAR points cloud instead of an image
captured by a camera. This method first uses the deeplabv3+ [\ref{ref:1}]
network to segment the image captured by the camera, and the segmentation
result is merged with the point clouds collected by the LIDAR as the input of
the proposed network. In this neural network, we also add a long short-term
memory (LSTM) structure to assist the network for semantic segmentation of lane
markings by using the the time series information. The experiments on more than
14,000 image datasets which we have manually labeled and expanded have shown
the proposed method has better performance on the semantic segmentation of the
points cloud bird's eye view. Therefore, the automation of high-precision map
construction can be significantly improved. Our code is available at
https://github.com/rolandying/FusionLane.
