Monthly summary for 2026-04: Delivered the Planning Factor Interface for the Diffusion Planner in technolojin/autoware.universe, enabling detection of stop and slowdown factors in trajectories. Refactored the diffusion_planner module for better organization and maintainability. Added comprehensive tests to validate the new interface and ensure regression protection. Aligned default parameters to improve stability and reduce configuration drift. Performed targeted fixes and test updates to stabilize CI.

February 2026 — Stability improvement in autoware.universe by fixing Goal Planner deadlock and enhancing thread safety. Implemented mutex-based protection around shared data in the goal planner to prevent data races, resulting in more reliable planning under concurrent workloads and reduced risk of runtime stalls.

December 2025: Implemented a C++ Perception Replayer for autoware_tools, enabling rosbag-based replay of perception data with configurable noise and route publishing. This work standardizes data replay workflows, improves test coverage, and supports reproducible experimentation across perception stacks. In addition to delivering the feature, I performed packaging and quality improvements including fixing pre-commit issues, cleaning up package.xml dependencies, and updating README with usage guidance. I also refined option handling and added error output for better observability and robustness. Delivered under PR #321 with the commits d0e3d8c1195cf94dd82d0be8044b1aec5dc5e536, and focused on maintainability and integration with routing workflows.

November 2025 monthly summary focusing on key accomplishments across vish0012/autoware.universe and autowarefoundation/autoware_tools. Delivered critical stability improvements and usability enhancements with strong cross-repo collaboration. Demonstrated solid CI hygiene, documentation updates, and maintainable code changes that reduce runtime risk and improve operator workflows.

Monthly Summary for 2025-10: Key features delivered: - autoware.universe: Static Obstacle Avoidance extended to handle parking violations and VRUs, with new parameters and data structures for识别 and handling (commits: c70d6a33db692a88c4c4589c7fb652db6b1f6a70). - autoware.universe: Stopped Road Users, Virtual Wall Handling, and Front-Polygon Collision Detection enhancements including tracking for stopped VRUs, ego arrival at virtual walls, improved stop obstacle filtering (allow passage after a duration), and front-facing polygon collision checks (front bumper) while excluding ego sides from detection area; updated documentation (commits: 65d562d6409072e870246213152a09b70a346df4; bb51db2617f080f017debafc1a0916734ec4828e; e1ad77f107d7727bfab894375610775c006c7a3c). - autoware.universe: Blind Spot Stop Line Generation Bug Fix to prioritize traffic light lines and avoid conflicts with instant stop lines (commit: 3fa41b1a5da23f885f55150d6b49b3b489dc48fb). - tier4/autoware_launch: Parking violation vehicle avoidance configuration introducing a new parameter to control ego response (auto/manual/ignore) for parking-violation vehicles (commit: 6a60a23ec89aba54f2ef45a89c58ec862ee94a27). - tier4/autoware_launch: Stopped road user tracking thresholds in road_user_stop configuration to refine stop criteria, positional tolerance, and ego/virtual wall proximity (commit: 0a79f18f553b1a9a08e9fb61016efae467e2bf08). - autoware.core: Public API exposure for calculate_error_poses by moving it to the header, enabling reuse across components and ego pose error estimation based on time-to-convergence (commit: c15de78f8a4b93ce21a7d26440eb8eb9c0032494). - tier4/tier4_ad_api_adaptor: Autoware API Extension expanded planning factors for safer routing, adding road_user_stop factor and three intersection-related factors (intersection collision checking, occlusion, rear collision checking) to the API extension configuration (commits: cb1634a6fe6f1e4b8bd82f73d2c9eaf11ca3b908; c90ecea7daf88d617f76ef2b33dbfaf6207fbbce). Major bugs fixed: - Blind Spot Stop Line Generation bug fix improved reliability and safety (commit: 3fa41b1a5da23f885f55150d6b49b3b489dc48fb). Overall impact and accomplishments: - Strengthened safety and planning capabilities across multiple repos with configurable behavior for parking-violation vehicles, VRUs, and complex intersections. - Enabled reuse and broader adoption through public API exposure and expanded API extension planning factors, improving cross-component integration and decision-making under time pressure. - Documentation updates accompany feature work to reduce onboarding time and enable consistent usage. Technologies/skills demonstrated: - C++, ROS-based module integration, and parameterization of runtime behavior. - API design and public exposure (headers) for cross-component reuse. - Front- and rear- angle collision detection strategies and virtual wall handling. - Cross-repo collaboration and feature-driven development with a focus on business safety and routing performance.

September 2025 monthly performance snapshot focusing on safety, planning quality, and visualization improvements across Autoware Foundation’s core repositories. The month delivered several high-impact features, critical bug fixes, and configurable capabilities that improve vehicle behavior, debugging visibility, and operational flexibility for different use cases. Key outcomes: - Planning robustness improved through targeted fixes and refinements in the autoware.universe planning stack. - Visualization and debugging capabilities expanded to better understand and tune behavior in unknown or dynamic scenarios. - Configurability introduced to adapt to different deployment needs without code changes.

Monthly summary for 2025-08: Key features delivered across autoware.universe and autoware_launch included configuration cleanups, visibility enhancements for safety factors, and stabilization of stop poses, with a visualization upgrade to PlanningFactor across RViz. These changes reduce configuration drift, improve safety and planning visibility, and standardize tooling across repos. Key improvements and commits are listed below.

July 2025 delivered key features to improve planning visibility, safety, and deployment reliability across core Autoware modules. PlanningFactor Reporting Enhancements were implemented across multiple planners (goal_planner, start_planner, lane_change, intersection_module, and static_obstacle_avoidance), adding richer PlanningFactor data, tests, and refined publish conditions. Duplicates and backward planning issues were addressed to ensure consistent data flow and easier debugging. The Road User Stop module was introduced to safely halt the ego vehicle when pedestrians, cyclists, or other road users appear in the planned trajectory, with parameterization for stop planning and obstacle filtering. Launch and configuration support for road_user_stop was added in autoware_launch to enable controlled stopping, opposing traffic handling, object filtering, and virtual wall visualization. These changes collectively enhance safety, analytics, and deployment readiness, delivering measurable business value and technical robustness.

June 2025 monthly summary focusing on crosswalk-related improvements across two Autoware repositories. Implemented crosswalk configuration parameter renaming for clarity with no behavioral changes in tier4/autoware_launch. In autowarefoundation/autoware.universe, renamed 'stuck vehicle' to 'obstruction prevention' in the crosswalk module and enhanced the PlanningFactor topic to include object IDs and stopping reasons, improving transparency in decision-making. This cycle also included documentation updates to reflect the new naming and parameter changes.