OpenHUTB/nn
Apr 2026 – Apr 2026
1 Month active
Languages Used
MarkdownPython
Technical Skills
Pythonautomated driving simulationautomated systemsautonomous vehicle developmentcomputer visiondata annotation
Nathenea1
PROFILE
Nathenea1
Developed two end-to-end features for the OpenHUTB/nn repository focused on automated driving simulation and data collection using Python and CARLA. Built a traffic sign data collection and annotation system that automated vehicle navigation across multiple weather scenarios, generating VOC-format datasets for model training. Delivered a vision-based traffic light detection module with real-time speed display and automated violation logging, supporting downstream annotation and evaluation. Integrated robust data pipelines and comprehensive documentation, enabling scalable dataset generation and streamlined onboarding. Emphasized automation and reproducibility throughout, stabilizing the data collection and labeling process to accelerate the development of autonomous vehicle safety models.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,678
Activity Months1
Your Network
263 peopleWork History
April 2026
3 Commits • 2 Features
Apr 1, 2026OpenHUTB/nn – April 2026: Accelerated data collection and violation-detection capabilities via CARLA-based and vision-based systems. Delivered two end-to-end features with integrated documentation, CARLA integration, and robust data pipelines enabling scalable dataset generation for training and evaluation of driving models. Demonstrated end-to-end automation across traffic-sign and traffic-light tasks, with multi-weather testing and VOC-format datasets, plus automated violation logging for downstream annotation and model training.
3 Commits • 2 Features
Apr 1, 2026OpenHUTB/nn – April 2026: Accelerated data collection and violation-detection capabilities via CARLA-based and vision-based systems. Delivered two end-to-end features with integrated documentation, CARLA integration, and robust data pipelines enabling scalable dataset generation for training and evaluation of driving models. Demonstrated end-to-end automation across traffic-sign and traffic-light tasks, with multi-weather testing and VOC-format datasets, plus automated violation logging for downstream annotation and model training.
April 2026
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage40.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Pythonautomated driving simulationautomated systemsautonomous vehicle developmentcomputer visiondata annotationdata collectiondocumentationimage processingsimulation
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