Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review
It provides a resource for researchers in autonomous driving to select appropriate datasets, though it is incremental as a review paper.
This paper reviews over 30 publicly available datasets for lane detection in autonomous driving, analyzing their characteristics and limitations to identify challenges and research gaps for future improvements.
Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation in 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 over 30 publicly available lane detection datasets, systematically analysing their characteristics, advantages and limitations. We classify these datasets based on key factors such as sensor resolution, annotation types and diversity of road and weather conditions. By identifying existing challenges and research gaps, we highlight opportunities for future dataset improvements that can further drive innovation in robust lane detection. This survey serves as a resource for researchers seeking appropriate datasets for lane detection, and contributes to the broader goal of advancing autonomous driving.