OpenHUTB/nn
Sep 2025 – Dec 2025
4 Months active
Languages Used
MarkdownPython
Technical Skills
Autonomous DrivingDeep Reinforcement LearningModel Predictive ControlTrajectory Predictionautonomous drivingcontrol systems
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PROFILE
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Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
5
Lines of code
3,027
Activity Months4
Your Network
159 people
Work History
December 2025
9 Commits • 2 Features
Dec 1, 2025Month 2025-12 — OpenHUTB/nn: Implemented core CARLA RL environment enhancements and expanded evaluation/navigation capabilities to accelerate development and assessment of autonomous driving policies. Focused on reliability, richer observation support, robust reward shaping, and ROS-based deployment for end-to-end workflows. The work improves experiment reproducibility, policy benchmarking, and deployment readiness with richer telemetry and trajectory logging.
9 Commits • 2 Features
Dec 1, 2025Month 2025-12 — OpenHUTB/nn: Implemented core CARLA RL environment enhancements and expanded evaluation/navigation capabilities to accelerate development and assessment of autonomous driving policies. Focused on reliability, richer observation support, robust reward shaping, and ROS-based deployment for end-to-end workflows. The work improves experiment reproducibility, policy benchmarking, and deployment readiness with richer telemetry and trajectory logging.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025Month 2025-11 summary focusing on delivering business value and technical achievements in OpenHUTB/nn. Key outcomes include the delivery of a Hybrid Defense System for Autonomous Driving (DRL + MPC) trajectory prediction, documentation hygiene improvements (README rename and removal of obsolete README), and ongoing maintenance to keep the codebase stable. No major bugs fixed this month; minor issues addressed as part of integration work. Technologies demonstrated: DRL, MPC, end-to-end planning pipelines, and documentation governance.
November 2025
1 Commits • 1 Features
Nov 1, 2025Month 2025-11 summary focusing on delivering business value and technical achievements in OpenHUTB/nn. Key outcomes include the delivery of a Hybrid Defense System for Autonomous Driving (DRL + MPC) trajectory prediction, documentation hygiene improvements (README rename and removal of obsolete README), and ongoing maintenance to keep the codebase stable. No major bugs fixed this month; minor issues addressed as part of integration work. Technologies demonstrated: DRL, MPC, end-to-end planning pipelines, and documentation governance.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary focusing on key accomplishments in OpenHUTB/nn. Primary work centered on documenting end-to-end trajectory prediction for the Car_Dinoag module, enabling easier onboarding and future feature integration. No major bug fixes were completed this month; emphasis on documentation, architectural clarity, and setting groundwork for DRL+MPC-based autonomous driving trajectory prediction. Potential business impact includes improved maintainability, clearer handoffs, and faster integration of DRL+MPC concepts.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary focusing on key accomplishments in OpenHUTB/nn. Primary work centered on documenting end-to-end trajectory prediction for the Car_Dinoag module, enabling easier onboarding and future feature integration. No major bug fixes were completed this month; emphasis on documentation, architectural clarity, and setting groundwork for DRL+MPC-based autonomous driving trajectory prediction. Potential business impact includes improved maintainability, clearer handoffs, and faster integration of DRL+MPC concepts.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered foundational documentation for a Hybrid Collision Avoidance System in the OpenHUTB/nn repository. The feature introduces a README outlining how a hybrid approach combines deep reinforcement learning with model predictive control to enhance collision avoidance, and establishes the initial documentation scaffold to support future implementation, experimentation, and cross-team collaboration. This work lays the groundwork for measurable safety improvements and accelerates onboarding for new contributors.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered foundational documentation for a Hybrid Collision Avoidance System in the OpenHUTB/nn repository. The feature introduces a README outlining how a hybrid approach combines deep reinforcement learning with model predictive control to enhance collision avoidance, and establishes the initial documentation scaffold to support future implementation, experimentation, and cross-team collaboration. This work lays the groundwork for measurable safety improvements and accelerates onboarding for new contributors.
Activity
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Quality Metrics
Correctness83.4%
Maintainability83.4%
Architecture83.4%
Performance83.4%
AI Usage48.2%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
AIAutonomous DrivingAutonomous SystemsCARLAData LoggingDeep Reinforcement LearningMachine LearningModel Predictive ControlPythonPython ProgrammingROSReinforcement LearningRoboticsSimulationTrajectory Prediction
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