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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.00769 |
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| _version_ | 1866913525616607232 |
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| author | Krajewski, Robert Kim, Huijo |
| author_facet | Krajewski, Robert Kim, Huijo |
| contents | High-definition maps (HD maps) play a crucial role in the development, safety validation, and operation of highly automated vehicles. Efficiently collecting up-to-date sensor data from road segments and obtaining accurate maps from these are key challenges in HD map creation. Commonly used methods, such as dedicated measurement vehicles and crowd-sourced data from series vehicles, often face limitations in commercial viability. Although high-resolution aerial imagery offers a cost-effective or even free alternative, it requires significant manual effort and time to transform it into maps. In this paper, we introduce a semi-automatic method for creating HD maps from high-resolution aerial imagery. Our method involves training neural networks to semantically segment aerial images into classes relevant to HD maps. The resulting segmentation is then hierarchically post-processed to generate a prototypical HD map of visible road elements. Exporting the map to the Lanelet2 format allows easy extension for different use cases using standard tools. To train and evaluate our method, we created a dataset using public aerial imagery of urban road segments in Germany. In our evaluation, we achieved an automatic mapping of lane markings and road borders with a recall and precision exceeding 96%. The source code for our method is publicly available at https://github.com/RobertKrajewski/DeepAerialMapper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00769 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles Krajewski, Robert Kim, Huijo Computer Vision and Pattern Recognition High-definition maps (HD maps) play a crucial role in the development, safety validation, and operation of highly automated vehicles. Efficiently collecting up-to-date sensor data from road segments and obtaining accurate maps from these are key challenges in HD map creation. Commonly used methods, such as dedicated measurement vehicles and crowd-sourced data from series vehicles, often face limitations in commercial viability. Although high-resolution aerial imagery offers a cost-effective or even free alternative, it requires significant manual effort and time to transform it into maps. In this paper, we introduce a semi-automatic method for creating HD maps from high-resolution aerial imagery. Our method involves training neural networks to semantically segment aerial images into classes relevant to HD maps. The resulting segmentation is then hierarchically post-processed to generate a prototypical HD map of visible road elements. Exporting the map to the Lanelet2 format allows easy extension for different use cases using standard tools. To train and evaluate our method, we created a dataset using public aerial imagery of urban road segments in Germany. In our evaluation, we achieved an automatic mapping of lane markings and road borders with a recall and precision exceeding 96%. The source code for our method is publicly available at https://github.com/RobertKrajewski/DeepAerialMapper. |
| title | DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.00769 |