ARUSfs/DRIVERLESS2
Nov 2024 – Jul 2025
7 Months active
Languages Used
C++CMakePythonYAMLc++yaml
Technical Skills
Algorithm DesignAlgorithm DevelopmentAlgorithm ImplementationAlgorithm OptimizationBuild System ConfigurationC++
David Guil Barranco
PROFILE
David Guil Barranco
David Guil built and enhanced autonomous planning and perception modules for the ARUSfs/DRIVERLESS2 repository, focusing on robust trajectory generation and reliable cone detection for driverless vehicles. He developed ROS and ROS 2 nodes in C++ and Python, implementing algorithms such as RANSAC and DBSCAN for path extraction and cone localization from point cloud data. David introduced weighted scoring and tunable parameters to improve detection accuracy and planning flexibility, while refactoring modules for maintainability and debugging. His work emphasized end-to-end data flow, configuration management, and real-world reliability, resulting in safer, more adaptable autonomous navigation and streamlined development cycles.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
28Total
Bugs
0
Commits
28
Features
11
Lines of code
3,039
Activity Months7
Your Network
14 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Delivered a new acceleration planning capability for ARUSfs/DRIVERLESS2. Implemented a ROS node named 'ebs_test' that uses a ConeXYZColorScore-based perception input to generate a trajectory with speed and acceleration profiles. Introduced a custom point cloud structure ConeXYZColorScore and wired an end-to-end flow: perception data -> trajectory planning -> published trajectory. This feature is backed by commit bfe1d64049b51f83d83e36b466f9d5076a481794 (feat: add files).
1 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Delivered a new acceleration planning capability for ARUSfs/DRIVERLESS2. Implemented a ROS node named 'ebs_test' that uses a ConeXYZColorScore-based perception input to generate a trajectory with speed and acceleration profiles. Introduced a custom point cloud structure ConeXYZColorScore and wired an end-to-end flow: perception data -> trajectory planning -> published trajectory. This feature is backed by commit bfe1d64049b51f83d83e36b466f9d5076a481794 (feat: add files).
July 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for ARUSfs/DRIVERLESS2 focusing on cone-detection enhancements via weighted RANSAC, with real-world impact on perception robustness and navigation safety. No major bugs fixed in this period.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for ARUSfs/DRIVERLESS2 focusing on cone-detection enhancements via weighted RANSAC, with real-world impact on perception robustness and navigation safety. No major bugs fixed in this period.
May 2025
4 Commits • 2 Features
May 1, 2025May 2025 monthly summary for ARUSfs/DRIVERLESS2: Focused on refactoring and enhancing planning modules with improved readability, robust debugging, and tunable configuration to accelerate iteration, reduce troubleshooting time, and improve deployment confidence.
4 Commits • 2 Features
May 1, 2025May 2025 monthly summary for ARUSfs/DRIVERLESS2: Focused on refactoring and enhancing planning modules with improved readability, robust debugging, and tunable configuration to accelerate iteration, reduce troubleshooting time, and improve deployment confidence.
May 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Strengthened Graph SLAM data association robustness in ARUSfs/DRIVERLESS2 by tuning ICP parameters within the Euclidean data association to enhance reliability in challenging environments. This included increasing maximum iterations, Euclidean fitness epsilon, and transformation epsilon, and setting a maximum correspondence distance to reduce false associations. Implemented via commit 27bc85e77ef74620128d10cd3265477225e4c93f with message 'feat: relax thresholds'. Business impact: more reliable localization and mapping in difficult environments, enabling safer autonomous operation and reducing post-processing debugging effort.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Strengthened Graph SLAM data association robustness in ARUSfs/DRIVERLESS2 by tuning ICP parameters within the Euclidean data association to enhance reliability in challenging environments. This included increasing maximum iterations, Euclidean fitness epsilon, and transformation epsilon, and setting a maximum correspondence distance to reduce false associations. Implemented via commit 27bc85e77ef74620128d10cd3265477225e4c93f with message 'feat: relax thresholds'. Business impact: more reliable localization and mapping in difficult environments, enabling safer autonomous operation and reducing post-processing debugging effort.
January 2025
5 Commits • 2 Features
Jan 1, 2025January 2025 focused on strengthening trajectory planning for ARUSfs/DRIVERLESS2. Delivered enhancements to Adaptive Cruise Control (ACC) planning with configurable speed/acceleration/deceleration profiles and centralized profile generation to improve responsiveness and safety. Skidpad planning received realistic speed/acceleration profiles with X/Y constraints, per-section speed data, and initialization of acceleration profiles. Exposed tunable parameters (acceleration, step width) to improve control fidelity and facilitate rapid iteration. No major bugs fixed this month; stability improvements and maintainability gains from centralizing profile generation and parameter exposure. The work delivers direct business value through safer, more predictable autonomous driving behavior and faster tuning cycles.
5 Commits • 2 Features
Jan 1, 2025January 2025 focused on strengthening trajectory planning for ARUSfs/DRIVERLESS2. Delivered enhancements to Adaptive Cruise Control (ACC) planning with configurable speed/acceleration/deceleration profiles and centralized profile generation to improve responsiveness and safety. Skidpad planning received realistic speed/acceleration profiles with X/Y constraints, per-section speed data, and initialization of acceleration profiles. Exposed tunable parameters (acceleration, step width) to improve control fidelity and facilitate rapid iteration. No major bugs fixed this month; stability improvements and maintainability gains from centralizing profile generation and parameter exposure. The work delivers direct business value through safer, more predictable autonomous driving behavior and faster tuning cycles.
January 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ARUSfs/DRIVERLESS2: Implemented Skidpad Planning Enhancements with DBSCAN-based center detection for robust cone localization and refined configuration parameters for track template and speed profile initialization, enabling more flexible and accurate skidpad trajectories. These improvements enhance testing reliability and accelerate validation of autonomous behaviors on skidpad setups.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ARUSfs/DRIVERLESS2: Implemented Skidpad Planning Enhancements with DBSCAN-based center detection for robust cone localization and refined configuration parameters for track template and speed profile initialization, enabling more flexible and accurate skidpad trajectories. These improvements enhance testing reliability and accelerate validation of autonomous behaviors on skidpad setups.
November 2024
14 Commits • 3 Features
Nov 1, 2024During 2024-11, delivered a cohesive autonomous planning upgrade for ARUSfs/DRIVERLESS2 by advancing three core planning modules: acceleration planning, route planning, and skidpad planning. Implemented end-to-end data flow from perception to planning in ROS 2, with iterative refinements to perception integration, RANSAC-based path extraction, and trajectory generation. Stabilized build/configuration and documentation through setup fixes and subscriber integration, enabling faster deployment and testing of autonomous behaviors.
14 Commits • 3 Features
Nov 1, 2024During 2024-11, delivered a cohesive autonomous planning upgrade for ARUSfs/DRIVERLESS2 by advancing three core planning modules: acceleration planning, route planning, and skidpad planning. Implemented end-to-end data flow from perception to planning in ROS 2, with iterative refinements to perception integration, RANSAC-based path extraction, and trajectory generation. Stabilized build/configuration and documentation through setup fixes and subscriber integration, enabling faster deployment and testing of autonomous behaviors.
November 2024
Activity
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Quality Metrics
Correctness80.8%
Maintainability81.8%
Architecture77.0%
Performance67.2%
AI Usage21.4%
Skills & Technologies
Programming Languages
C++CMakePythonYAMLc++yaml
Technical Skills
Algorithm DesignAlgorithm DevelopmentAlgorithm ImplementationAlgorithm OptimizationAutonomous DrivingBuild System ConfigurationC++C++ DevelopmentCMakeComputer VisionConfiguration ManagementControl SystemsPath PlanningPerceptionPlanning
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