OpenHUTB/nn
Sep 2025 – Dec 2025
4 Months active
Languages Used
C++MarkdownPythonCMake
Technical Skills
Autonomous DrivingCARLA SimulatorComputer VisionDeep LearningDocumentationObject Detection
William4861
PROFILE
William4861
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
24Total
Bugs
1
Commits
24
Features
7
Lines of code
8,497
Activity Months4
Your Network
159 people
Work History
December 2025
2 Commits • 1 Features
Dec 1, 2025Concise monthly summary for 2025-12: OpenHUTB/nn delivered a ROS-based architecture migration and project refactor with new traffic-generation and object-detection nodes. The repository structure was reorganized, legacy scripts were archived to reduce technical debt, and documentation (including installation steps) was updated to reflect the new features and deployment workflow. This work establishes a scalable, maintainable foundation for future features and easier onboarding for new developers.
2 Commits • 1 Features
Dec 1, 2025Concise monthly summary for 2025-12: OpenHUTB/nn delivered a ROS-based architecture migration and project refactor with new traffic-generation and object-detection nodes. The repository structure was reorganized, legacy scripts were archived to reduce technical debt, and documentation (including installation steps) was updated to reflect the new features and deployment workflow. This work establishes a scalable, maintainable foundation for future features and easier onboarding for new developers.
December 2025
November 2025
14 Commits • 1 Features
Nov 1, 2025Monthly performance summary for 2025-11 (OpenHUTB/nn): Delivered a consolidated Autonomous Vehicle Object Detection System across CARLA scenes, integrating YOLOv3 and YOLOv3-Tiny detectors with data collection, and enabling training with YOLOv5/YOLOv8, evaluation, and documentation improvements. Implemented end-to-end data collection and large-model training capabilities, including training pause/resume support, and completed a 50-epoch training run with precision improvements. Achieved notable stability and quality gains through extensive core-code refactors and conflict resolutions, with fixes for detection box issues and restoration of evaluation/training workflows. Enhanced repository health and developer experience via documentation polishing, README.md refinements, and dependency updates (requirements.txt). Demonstrated business value through increased perception reliability in simulation, faster iteration cycles, and clearer, reproducible workflows that ready the project for deployment and further optimization.
November 2025
14 Commits • 1 Features
Nov 1, 2025Monthly performance summary for 2025-11 (OpenHUTB/nn): Delivered a consolidated Autonomous Vehicle Object Detection System across CARLA scenes, integrating YOLOv3 and YOLOv3-Tiny detectors with data collection, and enabling training with YOLOv5/YOLOv8, evaluation, and documentation improvements. Implemented end-to-end data collection and large-model training capabilities, including training pause/resume support, and completed a 50-epoch training run with precision improvements. Achieved notable stability and quality gains through extensive core-code refactors and conflict resolutions, with fixes for detection box issues and restoration of evaluation/training workflows. Enhanced repository health and developer experience via documentation polishing, README.md refinements, and dependency updates (requirements.txt). Demonstrated business value through increased perception reliability in simulation, faster iteration cycles, and clearer, reproducible workflows that ready the project for deployment and further optimization.
October 2025
3 Commits • 3 Features
Oct 1, 2025OpenHUTB/nn – 2025-10 Monthly Summary. Delivered two major features for the autonomous vehicle testing pipeline and enhanced project documentation to accelerate onboarding and collaboration. Focus areas included synthetic traffic generation in CARLA, an end-to-end object detection pipeline using YOLOv3, and a new README reference section with external resources. Commit-level improvements also included consistent README formatting across the project to improve readability and reduce onboarding time. No critical defects reported; minor documentation formatting fixes were applied to improve clarity and maintainability.
3 Commits • 3 Features
Oct 1, 2025OpenHUTB/nn – 2025-10 Monthly Summary. Delivered two major features for the autonomous vehicle testing pipeline and enhanced project documentation to accelerate onboarding and collaboration. Focus areas included synthetic traffic generation in CARLA, an end-to-end object detection pipeline using YOLOv3, and a new README reference section with external resources. Commit-level improvements also included consistent README formatting across the project to improve readability and reduce onboarding time. No critical defects reported; minor documentation formatting fixes were applied to improve clarity and maintainability.
October 2025
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for OpenHUTB/nn. Key features delivered: - Humanoid Perception Documentation and Structure: established documentation hygiene, README standardization, and markdown formatting improvements for the Humanoid Perception module, enabling clearer contribution guidelines and faster onboarding. Commits include 969db8c40633e46c1d8a354665270d4998e64b69, 24f868e0c1556b6cf72facab86b954148c189a3c, and 6bb540a3f753a7b013cc549206df5606e8cdfb29. - Autonomous Vehicle Perception and Trajectory Planning: initial implementation for object detection using YOLOv3 and trajectory planning with CARLA, establishing a perception-to-simulation feedback loop for validation and experimentation. Commit e765973605d855f31ebe130401f45013773432a3. Major bugs fixed: - Humanoid Perception Directory Name Fix: resolved issues from non-ASCII folder name by renaming directory from humanoid_wangyuan to humanoid_perception, improving cross-platform compatibility and import stability. Commit fae6a5d601a7f7d8654be786a3adef853a99c67a. Overall impact and accomplishments: - Improved maintainability, documentation quality, and onboarding for the Humanoid Perception module; increased cross-platform reliability with a clean directory structure; established an end-to-end perception prototype linked to a driving simulator for future validation and iteration. Technologies/skills demonstrated: - Documentation engineering, Markdown formatting, and repository hygiene. - Computer vision (YOLOv3) and autonomous driving simulation (CARLA) prototyping. - Module restructuring and cross-module integration readiness (submodule removal, naming normalization).
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for OpenHUTB/nn. Key features delivered: - Humanoid Perception Documentation and Structure: established documentation hygiene, README standardization, and markdown formatting improvements for the Humanoid Perception module, enabling clearer contribution guidelines and faster onboarding. Commits include 969db8c40633e46c1d8a354665270d4998e64b69, 24f868e0c1556b6cf72facab86b954148c189a3c, and 6bb540a3f753a7b013cc549206df5606e8cdfb29. - Autonomous Vehicle Perception and Trajectory Planning: initial implementation for object detection using YOLOv3 and trajectory planning with CARLA, establishing a perception-to-simulation feedback loop for validation and experimentation. Commit e765973605d855f31ebe130401f45013773432a3. Major bugs fixed: - Humanoid Perception Directory Name Fix: resolved issues from non-ASCII folder name by renaming directory from humanoid_wangyuan to humanoid_perception, improving cross-platform compatibility and import stability. Commit fae6a5d601a7f7d8654be786a3adef853a99c67a. Overall impact and accomplishments: - Improved maintainability, documentation quality, and onboarding for the Humanoid Perception module; increased cross-platform reliability with a clean directory structure; established an end-to-end perception prototype linked to a driving simulator for future validation and iteration. Technologies/skills demonstrated: - Documentation engineering, Markdown formatting, and repository hygiene. - Computer vision (YOLOv3) and autonomous driving simulation (CARLA) prototyping. - Module restructuring and cross-module integration readiness (submodule removal, naming normalization).
Activity
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Quality Metrics
Correctness85.0%
Maintainability84.2%
Architecture85.8%
Performance83.4%
AI Usage40.8%
Skills & Technologies
Programming Languages
C++CMakeMarkdownPython
Technical Skills
Autonomous DrivingAutonomous SystemsAutonomous VehiclesC++CARLACARLA SimulatorComputer VisionData CollectionData LoggingData VisualizationDeep LearningDocumentationMachine LearningModel TrainingObject Detection
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