October 2025 monthly summary for tier4/driving_log_replayer_v2: focused on reliability, configurability, and extended diagnostics. Delivered configurability with Publish Profile for Log Replay to selectively publish topics during replay, enhanced obstacle segmentation visualization with MultiPolygon support and improved robustness against invalid geometries, and expanded diagnostic judgments to support duration and percentage-based criteria. Fixed critical issues in YAML profile retrieval and empty planning_factors handling to ensure consistent data processing and replay results. Updated tests and configurations to reflect new capabilities.

September 2025 — tier4/driving_log_replayer_v2 monthly summary. Delivered substantial real-time feedback, refined evaluation logic, and flexible simulation capabilities, with targeted fixes to improve correctness and visualization for localization tests. Key features include enabling ROS-based real-time publishing of evaluation conditions and planning factor results with live feedback in the driving log replayer, refining planning factor evaluation logic to store/process factors more accurately, and adding launch-time overrides for vehicle and sensor models to support varied simulation or operation scenarios. Localization visualization was enhanced by expanding the topic list to include vector map markers, point clouds, and system processing time metrics. A critical bug fix was implemented to correct PlanningFactor result initialization by introducing a 'success' attribute and ensuring proper frame initialization. These changes accelerate feedback loops, improve evaluation fidelity, and broaden testing coverage, delivering clear business value in faster iteration cycles and more robust simulation capabilities.

August 2025 focused on boosting reliability, robustness, and cross-module consistency across core Autoware repositories. Key work delivered improves real-time status visibility in planning and velocity smoothing, hardens perception against empty/no-ground clouds, and establishes a scalable framework for standardized condition-based evaluation. The efforts also address correctness of topic remapping in planning control, delivering measurable business value through safer, more maintainable software delivery.

July 2025 monthly summary for tier4/scenario_simulator_v2: Delivered a targeted bug fix in the Detection Sensor Simulation to improve orientation availability mapping across vehicle subtypes. The change assigns SIGN_UNKNOWN for CAR, TRUCK, BUS, and TRAILER and UNAVAILABLE for other unhandled subtypes, refining how sensor data is simulated. Implemented in commit d8487e0432e18e597ca731e183d5a0547ba9f319, following Lee-san's suggestion. Business value: more realistic sensor outputs, reduced edge-case discrepancies, and more reliable downstream testing and model evaluation. Technologies and skills demonstrated include debugging, data modeling for sensor simulations, and Git-based collaboration.

June 2025 monthly summary focused on delivering robust control evaluation capabilities, safety-oriented metrics, and reliability improvements across Autoware Universe, scenario simulator, and tooling. Key features delivered include: 1) Control Evaluator improvements with enhanced data dependency handling, expanded dys deviation metrics (non-absolute and absolute), refined onTimer subscriptions, and updated stop deviation logic to consider the nearest stop decision. 2) Added closest_object_distance metric to the control evaluator with optional threshold filtering to improve safety-related evaluation. 3) Introduced 95th and 99th percentile processing time metrics using a t-digest for finer-grained performance monitoring. 4) Fixed sensor orientation availability handling in scenario_simulator_v2 to correctly classify entity subtypes (BICYCLE, MOTORCYCLE, PEDESTRIAN, UNKNOWN -> UNAVAILABLE; others -> SIGN_UNKNOWN). 5) Fixed Perception Replayer loading of db3 ROS bags by mapping file extensions to correctly identify and load ROS bag files for the db3 format. These changes were implemented across autoware.universe, tier4/scenario_simulator_v2, and autoware_tools. Impact and value: improved safety evaluation accuracy and robustness of the control pipeline, enhanced performance visibility with targeted percentile metrics, and increased reliability of data loading pipelines, contributing to faster issue detection and higher confidence in automated driving deployments. Technologies/skills demonstrated: refactoring for clarity and robustness, metrics instrumentation and instrumentation architecture, t-digest based percentile analytics, threshold-based evaluation, safety-oriented metric design, ROS bag processing, and cross-repo collaboration.

May 2025 monthly summary for autoware.universe. Focused on stabilizing planning evaluation by fixing the abnormal stop metric and refactoring the stop decision logic. No new features were released this month; primary work delivered a bug fix that improves the accuracy of abnormal stop evaluation and a refactor to the stop decision flow, enhancing maintainability and safety in planning scenarios.

April 2025 monthly summary focusing on stabilizing signaling and expanding metrics-driven evaluation for planning and control. Key reliability fixes were implemented to ensure turn indicators are disabled when NO_COMMAND, eliminating ambiguous signals in both the simulator and planning contexts. This was complemented by substantial metrics enhancements that improve planning quality assessment and control performance evaluation.

March 2025 monthly summary for autoware.universe: Implemented a new Stop Deviation metric in the Control Evaluator to quantify the ego vehicle's deviation from the stop line when halting due to a planning factor. The change introduces parameter-driven evaluation, a subscriber pathway for planning factors, and the metric calculation logic, enabling more precise assessment of stop-line adherence and facilitating targeted tuning of planning modules.

February 2025 monthly summary for tier4/scenario_simulator_v2: Delivered Sensor Noise Modeling Enhancement for Detection System. Refactored noise modeling to replace Bernoulli distribution noises with a Markov process, updated configuration parameters, and renamed 'rho' to 'phi' for consistency. Result: improved accuracy and fidelity of sensor noise simulations, leading to more reliable detection results in QA/testing environments. All changes are tracked under repo tier4/scenario_simulator_v2, with commit b8ae6ca87b3bed84c4a66c695308962950863cdc.

Month: 2025-01 — Focused on enhancing control evaluation observability and steering metrics in the Autoware Control Evaluator. Delivered Control Evaluator Enhancements: added lane boundary proximity metrics and steering performance metrics, and refactored the evaluator to subscribe to behavior path and leverage vehicle information for richer data and validation. Key commits include: bca90301b2a0c142402ec59d1e59f78452bc58a9 (feat: add new boundary_distance metrics) and 8b0b9a7223278e72607f2da2b9f76ceaadc5f30a (feat: add new steering metrics). No major bugs reported this month. Impact: improved safety validation, observability, and data-driven tuning capabilities; supports more precise evaluation of control decisions and steering responsiveness. Technologies/skills demonstrated: C++, ROS/Autoware framework, metric instrumentation, telemetry/logging, control-evaluator refactoring, integration with behavior path and vehicle information.

December 2024 monthly summary for tier4/autoware_launch focusing on the RViz ego-model visualization reliability improvement. Delivered a targeted bug fix by updating the robot description topic QoS from Best Effort to Reliable to ensure all messages are delivered, resulting in more accurate and stable ego-model displays in RViz. The change addresses visualization inconsistencies and reduces operator confusion during planning and debugging. Commit reference and issue linkage provided.

Month 2024-11 monthly summary focusing on business value and technical achievements. In November, the team delivered foundational improvements to metrics, launch flexibility, and data utilities, while stabilizing runtime behavior in perception tooling. Key features delivered include: 1) Standardize Metric Reporting Across Evaluators and Planners: refactored modules to publish metrics via tier4_metric_msgs::MetricArray for consistent reporting in simulations and analyses (commits: 5cd47a78bcd7879811952724a49ff101b55e8eed). 2) Dynamic Launch Control via Preset Configuration: added preset.yaml driven control to enable/disable control modules for flexible system management (commit: ef36c36d160a1c2a15d5064e058c4e267718f1c3). 3) Accumulator Utility Refactor and Library Centralization: rename Stat to Accumulator and move to autoware_universe_utils, with tests validating functionality (commit: 12a86f63dd865c4a0661286c5b4f354260299944). 4) Metrics Output Control and Formatting: introduced configurable metrics output to log folder as JSON on shutdown, with trigger-based control and test fixes (commits: 35048a9fd48500d3c554df024475f910a2203633; b77f091e016585a8c1517006be944eda051d98cd; be4c9e01740910e9251f255a9a913c96fa2c3b9f). 5) Minor bug fix: Perception Replayer corrected to initialize rclpy once in the main block to prevent multiple initializations (commit: 2e13bf214cb83b51057fdbd3a168d14609416341). Overall impact and accomplishments: These changes improve data quality and observability, increase configurability of module launches, reduce maintenance burden through central utilities, and enhance runtime reliability across simulation and perception tooling. This supports safer experimentation, faster iteration, and clearer metrics-driven decision making for stakeholders. Technologies/skills demonstrated: ROS 2 development, tier4_metric_msgs integration, YAML-based presets, centralized utility design, test-driven development, Python rclpy initialization patterns, and JSON-based metrics logging.