# High-Definition-Map-Modeling-for-Autonomous-Driving

**Repository Path**: nealliu/High-Definition-Map-Modeling-for-Autonomous-Driving

## Basic Information

- **Project Name**: High-Definition-Map-Modeling-for-Autonomous-Driving
- **Description**: No description available
- **Primary Language**: Matlab
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2019-02-23
- **Last Updated**: 2022-04-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Introductioin
This is a research project supported by [HERE](https://www.here.com/en) company.
Therefore, the datasets(which will be published very soon) and some codes of the algorithm are confidential so won't be shown in this repo(the .If you want to learn more about the dataset and algorithm, please read this project's paper [Lane Boundary Extraction from Satellite Imagery](AUTONOMOUSGIS_2017_paper_3.pdf), which is accepted by ACM SIGSPATIAL Workshop 2017. However, we will provide some useful codes and tools such as satellite map retriving from Bing Map，diffrent coordinates transforamtion， evluation algorithm and a high-level easy-plug-in deep learning toolbox to deal with images written by tensorflow


Automated driving is becoming a reality. In this new reality, High
Definition (HD) Maps play an important role in path planning and
vehicle localization. Lane boundary geometry is one of the key components
of an HD Map. Such maps are typically created from ground
level LiDAR and imagery data, while useful in many ways, have
many limitations such as prohibitive cost, infrequent update, traffic
occlusions, and incomplete coverage. In this project, we propose
a novel method to automatically extract lane boundary from satellite
imagery using pixel-wise segmentation and machine learning,
and convert unstructured lines into structured road model by using
hypothesis linking algorithm, which addresses the aforementioned
limitations. We will also publish our experiment dataset consisting of
satellite imagery and the corresponding lane boundaries as ground
truth to train, test, and evaluate algorithms in the future.