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, which expanded the system’s ability to interpret sensor data and improved dataset coverage for training and testing. Logan also integrated Model Predictive Control (MPC) for trajectory rendering, enabling planning validation and visualization directly on the track. Using Python and leveraging skills in computer vision, control systems, and data visualization, his work provided deeper insight into sensor outputs and planning scenarios, laying groundwork for more robust simulation and reinforcement learning workflows.
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 people
Work 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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