PDM4AR/exercises
Nov 2024 – Oct 2025
4 Months active
Languages Used
PythonYAMLMarkdownDockerfileTOML
Technical Skills
Algorithm DesignAutonomous DrivingPythonRoboticsSimulationSoftware Engineering
Yueshan Li
PROFILE
Yueshan Li
Yue Shi Lee developed and maintained core features for the PDM4AR/exercises repository, focusing on autonomous highway driving planning and control. He implemented a reusable evaluation framework in Python and YAML, integrating agent interfaces, configuration files, and performance metrics to assess safety and efficiency in mixed traffic scenarios. His work included refining documentation, enhancing onboarding materials, and improving catalog maintainability through technical writing and code refactoring. Yue also addressed numerical analysis issues by applying robust tolerance checks, ensuring reliable validation. Through careful dependency management and Dockerfile updates, he supported seamless releases, demonstrating depth in software engineering and configuration management.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
5
Lines of code
216,637
Activity Months4
Your Network
39 people
Work History
October 2025
3 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for PDM4AR/exercises focused on delivering value through improved documentation, reliable exercise validation, and timely release readiness. Key features delivered include documentation improvements for the Dynamic Programming section and the finalization/release of Exercise 05 scheduling. Major bugs fixed involve strengthening exercise checks by applying absolute tolerance to numeric comparisons to prevent false negatives. Overall impact: clearer learner guidance, more robust validation, and faster onboarding for new contributors. Technologies/skills demonstrated include Python numerical checks (isclose with tolerance), documentation formatting, release management, and Git-based collaboration across a small, focused repo.
3 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for PDM4AR/exercises focused on delivering value through improved documentation, reliable exercise validation, and timely release readiness. Key features delivered include documentation improvements for the Dynamic Programming section and the finalization/release of Exercise 05 scheduling. Major bugs fixed involve strengthening exercise checks by applying absolute tolerance to numeric comparisons to prevent false negatives. Overall impact: clearer learner guidance, more robust validation, and faster onboarding for new contributors. Technologies/skills demonstrated include Python numerical checks (isclose with tolerance), documentation formatting, release management, and Git-based collaboration across a small, focused repo.
October 2025
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 performance summary for PDM4AR/exercises focusing on release readiness and documentation enhancements that improve reliability and onboarding for the 2025.0.0 release.
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 performance summary for PDM4AR/exercises focusing on release readiness and documentation enhancements that improve reliability and onboarding for the 2025.0.0 release.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for PDM4AR/exercises: Delivered documentation enhancements and catalog expansion, added Highway Driving exercise, and refined scoring/reporting for Exercise 12. These changes improve onboarding, analytics accuracy, and catalog maintainability. Key outcomes include clearer guidance for contributors, better metric integrity for player performance, and a scalable path for future catalog growth.
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for PDM4AR/exercises: Delivered documentation enhancements and catalog expansion, added Highway Driving exercise, and refined scoring/reporting for Exercise 12. These changes improve onboarding, analytics accuracy, and catalog maintainability. Key outcomes include clearer guidance for contributors, better metric integrity for player performance, and a scalable path for future catalog growth.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered end-to-end autonomous highway driving planning and control stack as part of the Fall 2024 Final Graded Exercise in PDM4AR/exercises. This included documentation, an agent interface, configuration files, and performance metrics to evaluate safe and efficient lane-changing maneuvers in mixed traffic. The work establishes a reusable evaluation framework and accelerates future testing and stakeholder reviews.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered end-to-end autonomous highway driving planning and control stack as part of the Fall 2024 Final Graded Exercise in PDM4AR/exercises. This included documentation, an agent interface, configuration files, and performance metrics to evaluate safe and efficient lane-changing maneuvers in mixed traffic. The work establishes a reusable evaluation framework and accelerates future testing and stakeholder reviews.
Activity
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Quality Metrics
Correctness90.8%
Maintainability90.0%
Architecture85.8%
Performance80.0%
AI Usage21.8%
Skills & Technologies
Programming Languages
DockerfileMarkdownPythonTOMLYAML
Technical Skills
Algorithm DesignAutonomous DrivingCI/CDCode RefactoringConfiguration ManagementCourse SetupDebuggingDependency ManagementDockerfileDocumentationNumerical AnalysisPerformance MetricsPythonRoboticsSimulation
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline