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A road surface image dataset with detailed annotations for driving assistance applications.
1State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China.
A new dataset of 370,151 road surface images aids autonomous vehicle development. This data enables better road sensing for enhanced safety and ride comfort, crucial for advanced driving systems.
Area of Science:
- Computer Vision
- Robotics
- Materials Science
Background:
- Autonomous vehicle safety and ride comfort depend on accurate road surface perception.
- Existing datasets may lack the diversity in road and weather conditions required for robust sensing.
Purpose of the Study:
- To introduce a large-scale, diverse dataset for road surface state analysis.
- To support the development of vision-based road sensing modules for autonomous driving.
Main Methods:
- Acquisition of 370,151 road surface images using vehicle-mounted cameras in China.
- Cropping of images to isolate road surface patches.
- Detailed annotation of images with friction level, material, and unevenness properties.
Main Results:
- A comprehensive dataset covering various road and weather conditions.
- Detailed annotations enabling granular analysis of road surface characteristics.
Conclusions:
- The dataset serves as a valuable resource for training and benchmarking vision-based road sensing algorithms.
- This data can significantly improve the performance of driving assistance systems and autonomous vehicle perception modules.
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