OpenHUTB/nn
Nov 2025 – Dec 2025
2 Months active
Languages Used
MarkdownPython
Technical Skills
PythonPython programmingalgorithm developmentalgorithm optimizationautonomous drivingautonomous navigation
cljrobot
PROFILE
Cljrobot
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
3
Lines of code
4,483
Activity Months2
Your Network
159 people
Work History
December 2025
6 Commits • 1 Features
Dec 1, 20252025-12 monthly summary for OpenHUTB/nn: Delivered Neural Network-Driven Autonomous Driving Improvements in CARLA. Implemented architecture simplification, enhanced recognition, improved obstacle detection/avoidance, throttle/brake tuning, and NPC collision handling, with comprehensive README/documentation updates. The work was executed through a sequence of commits focused on parameter refinements, recognition efficiency, collision avoidance, and code maintenance, resulting in a more robust and efficient autonomous driving simulation in CARLA. This release enhances safety, reliability, and performance, enabling faster iteration and more realistic testing.
6 Commits • 1 Features
Dec 1, 20252025-12 monthly summary for OpenHUTB/nn: Delivered Neural Network-Driven Autonomous Driving Improvements in CARLA. Implemented architecture simplification, enhanced recognition, improved obstacle detection/avoidance, throttle/brake tuning, and NPC collision handling, with comprehensive README/documentation updates. The work was executed through a sequence of commits focused on parameter refinements, recognition efficiency, collision avoidance, and code maintenance, resulting in a more robust and efficient autonomous driving simulation in CARLA. This release enhances safety, reliability, and performance, enabling faster iteration and more realistic testing.
December 2025
November 2025
10 Commits • 2 Features
Nov 1, 2025In 2025-11, OpenHUTB/nn delivered a robust upgrade to the CARLA multi-modal navigation system, focusing on sensor-integrated vehicle recognition, obstacle avoidance, throttle control, and recovery. Documentation improvements were implemented for core functionalities, environment setup, and usage instructions. These changes improve safety and reliability of autonomous navigation by increasing recognition accuracy, stabilizing throttle response, and shortening recovery times. Comprehensive documentation updates reduce onboarding time and provide clearer guidelines for engineering teams. Overall, the work enhances system resilience, accelerates iteration cycles, and supports safer operations in production environments.
November 2025
10 Commits • 2 Features
Nov 1, 2025In 2025-11, OpenHUTB/nn delivered a robust upgrade to the CARLA multi-modal navigation system, focusing on sensor-integrated vehicle recognition, obstacle avoidance, throttle control, and recovery. Documentation improvements were implemented for core functionalities, environment setup, and usage instructions. These changes improve safety and reliability of autonomous navigation by increasing recognition accuracy, stabilizing throttle response, and shortening recovery times. Comprehensive documentation updates reduce onboarding time and provide clearer guidelines for engineering teams. Overall, the work enhances system resilience, accelerates iteration cycles, and supports safer operations in production environments.
Activity
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Quality Metrics
Correctness81.2%
Maintainability81.2%
Architecture80.0%
Performance81.2%
AI Usage45.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
AI DevelopmentAutonomous SystemsComputer VisionDeep LearningMachine LearningPythonPython programmingRoboticsalgorithm developmentalgorithm optimizationautonomous drivingautonomous navigationautonomous vehicle developmentcomputer visiondeep learning
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