Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914603419566080 |
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| author | Gamerdinger, Jörg Teufel, Sven Bringmann, Oliver |
| author_facet | Gamerdinger, Jörg Teufel, Sven Bringmann, Oliver |
| contents | Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation across a variety of road scenarios. Numerous datasets have been introduced to support the development and evaluation of lane detection algorithms, each differing in terms of the amount of data, sensor types, annotation granularity, environmental conditions, and scenario diversity. This paper provides a comprehensive review of 20 publicly available lane detection datasets, systematically analyzing their characteristics, advantages, and limitations. We classify these datasets based on key performance indicators such as sensor resolution, annotation types and diversity of road and weather conditions using a novel multidimensional metric for dataset quality. By identifying existing challenges and research gaps, we highlight opportunities for future dataset improvements that can further drive innovation in robust lane detection. This review serves as a resource for researchers seeking appropriate datasets for robust lane detection and contributes to the broader goal of advancing autonomous driving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review Gamerdinger, Jörg Teufel, Sven Bringmann, Oliver Computer Vision and Pattern Recognition Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation across a variety of road scenarios. Numerous datasets have been introduced to support the development and evaluation of lane detection algorithms, each differing in terms of the amount of data, sensor types, annotation granularity, environmental conditions, and scenario diversity. This paper provides a comprehensive review of 20 publicly available lane detection datasets, systematically analyzing their characteristics, advantages, and limitations. We classify these datasets based on key performance indicators such as sensor resolution, annotation types and diversity of road and weather conditions using a novel multidimensional metric for dataset quality. By identifying existing challenges and research gaps, we highlight opportunities for future dataset improvements that can further drive innovation in robust lane detection. This review serves as a resource for researchers seeking appropriate datasets for robust lane detection and contributes to the broader goal of advancing autonomous driving. |
| title | Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.08540 |