# FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks

arXiv: https://arxiv.org/abs/2003.04404 · HTML: https://mlsanity.com/paper/2003.04404/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation](https://mlsanity.com/paper/2406.16072/) (2024)
- [Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems](https://mlsanity.com/paper/2104.04000/) (2021)
- [Homography Guided Temporal Fusion for Road Line and Marking Segmentation](https://mlsanity.com/paper/2404.07626/) (2024)
- [Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2504.08540/) (2025)

## Abstract (arXiv)

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+ [refref: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.