WE-Autopilot/Red-Team
Feb 2025 – Feb 2025
1 Month active
Languages Used
Python
Technical Skills
Code DocumentationComputer VisionControl SystemsData VisualizationMPCPython
LoganOT
PROFILE
Loganot
Logan Ouellette-Tran developed two core features for the WE-Autopilot/Red-Team repository, focusing on perception and planning validation in robotics. He enhanced lidar visualization by implementing multi-arrow rendering and refining drawing logic, while also expanding the dataset to improve training and testing coverage. Using Python and leveraging skills in computer vision and data visualization, Logan integrated Model Predictive Control (MPC) for trajectory rendering and planning validation, enabling on-track visualization and supporting documentation updates. The work demonstrated depth in simulation and control systems, providing a robust foundation for faster iteration and more transparent validation of sensor outputs and planning algorithms.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
2
Lines of code
565
Activity Months1
Your Network
12 peopleWork History
February 2025
9 Commits • 2 Features
Feb 1, 2025February 2025 monthly performance summary for WE-Autopilot/Red-Team. Delivered two major features targeting perception- and planning-level validation: (1) Lidar Visualization Enhancements with multi-arrow rendering, clarified drawing logic, and new lidar dataset files to expand training/testing coverage; (2) MPC-based trajectory rendering and control integration to support planning validation, trajectory visualization on track, and updated datasets/docs. These initiatives improved visibility into sensor outputs, expanded training material, and enabled MPC-informed planning scenarios for faster iteration and validation.
9 Commits • 2 Features
Feb 1, 2025February 2025 monthly performance summary for WE-Autopilot/Red-Team. Delivered two major features targeting perception- and planning-level validation: (1) Lidar Visualization Enhancements with multi-arrow rendering, clarified drawing logic, and new lidar dataset files to expand training/testing coverage; (2) MPC-based trajectory rendering and control integration to support planning validation, trajectory visualization on track, and updated datasets/docs. These initiatives improved visibility into sensor outputs, expanded training material, and enabled MPC-informed planning scenarios for faster iteration and validation.
February 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture75.6%
Performance71.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Code DocumentationComputer VisionControl SystemsData VisualizationMPCPythonReinforcement LearningRoboticsSimulationVisualization
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