rakadu1/Simulator - Simulator - Gitea: Git with a cup of tea

Author	SHA1	Message	Date
RUSHIL AMBARISH KADU	c568c11106	feat: Integrate C-SHENRON physics-based radar simulation pipeline Comprehensive integration of the Shenron radar model, enabling high-fidelity physics-driven radar synthesis from semantic LiDAR data. Core Changes: - Sensor Migration: Updated EGO vehicle to use 'sensor.lidar.ray_cast_semantic'. - Recorder Enhancement: Modified src/recorder.py to dynamically capture 6/7nd-column semantic LiDAR buffers [x, y, z, cos, obj, tag]. - Orchestration: Created scripts/ISOLATE/model_wrapper.py with the ShenronRadarModel class for a unified physics-to-detection API. - Processor Logic: Fixed broadcasting bugs and hardcoded antenna counts in RadarProcessor to support dynamic radar models (radarbook/ti_cascade). - Rich Data Output: Updated extraction logic to include [velocity, magnitude] parameters for ADAS performance analysis. - Batch Pipeline: Added scripts/generate_shenron.py to synthesize radar data as a post-processing step, maintaining simulation performance. - Visualization: Updated scripts/data_to_mcap.py to support the new /radar/shenron topic with a 5-field rich PointCloud schema. Stability & Fixes: - Implemented CUDA-enabled PyTorch checks with a robust CPU fallback. - Corrected CARLA 0.9.16 semantic column mapping in ISOLATE/lidar.py. - Handled indexing and range-slicing errors in the CFAR detection routine.	2 months ago
RUSHIL AMBARISH KADU	eea4c78b2f	feat(dashboard): implement GUI orchestrator and fix simulation I/O pipelines This commit introduces a Flask-based web dashboard for the BATL CARLA orchestrator and hardens the underlying data processing pipeline to support real-time UI tracking. Dashboard & GUI: - Added `dashboard/` directory containing the Flask backend (`app.py`), HTML UI, and static assets (CSS/JS) for a seamless browser-based simulation controller. - Created `dashboard.bat` to launch the web dashboard environment. Pipeline Stability (Unbuffered Streaming): - `run.bat`: Forced `PYTHONUNBUFFERED=1` to ensure subprocess stdout/stderr is immediately available. - `dashboard/app.py`: Injected the unbuffered environment flag into `subprocess.Popen` to prevent the UI console from freezing during heavy post-processing tasks. - `src/recorder.py` & `data_to_mcap.py`: Appended `flush=True` to stdout prints to guarantee immediate log propagation to the web dashboard text stream. Foxglove Data Integrity: - `data_to_mcap.py`: Hardened internal JSON schemas (`foxglove.Pose`, `foxglove.CompressedImage`, `foxglove.PointCloud`) by adding `$schema`, `$id`, and `title` metadata tags over draft-2020-12. This prevents Foxglove Studio from attempting internet CDN lookups, fully resolving the "Unable to parse ok response body as json" crashes when importing local MCAP sets.	2 months ago
RUSHIL AMBARISH KADU	15fbf8e31d	feat: Implement Deterministic Showcase Suite with Asynchronous Pipeline and Auto-MCAP This commit represents a major architectural upgrade to the CARLA ADAS simulation project, transitioning from stochastic Traffic Manager behavior to a fully deterministic, high-impact demo environment. 1. Deterministic Scenario Architecture New Showcase Scenario: Implemented scenarios/showcase.py for a manual "Left-Turn Across Path" (LTAP) maneuver. Physics Velocity Injection: Overrode engine/friction variance by directly setting set_target_velocity for NPC actors at 35 km/h. Multi-Stage Waypoint Guidance: Integrated EGO_MID and P1_MID transition points to ensure realistic, cinematic turning radii and reliable collision-avoidance timing. Manual Steering Controller: Developed a proportional steering-to-target helper for all actors. 2. Sensor & Data Pipeline Dual-Camera Support: Integrated a second Third-Person Perspective (TPP) camera. Radar Serialization Fix: Implemented trigonometric conversion of raw spherical coordinates (Depth, Azimuth, Altitude) into Cartesian XYZ + Velocity 16-byte PointClouds for Foxglove Studio. Synchronization: Aligned all sensor data (Dash Cam, TPP Cam, Radar, Lidar) at the orchestrator level. 3. Performance & Asynchronous I/O Asynchronous Recorder: Refactored src/recorder.py to use ThreadPoolExecutor, offloading cv2.imwrite operations to background threads to eliminate simulation stutter. Rapid Iteration: Added --no-record global flag to main.py for sub-second iteration during coordinate tuning. 4. Automation & Seamless UX Auto-MCAP conversion: Integrated data_to_mcap.py as a post-simulation callback in main.py . Auto-Video Stitching: Implement automatic .mp4 generation for both camera views using OpenCV's VideoWriter. Weather/Time CLI: Added --weather flag with presets (Clear, Rain, Sunset, Night). Console Sync: Integrated tqdm for professional progress tracking and refactored scenario logging to use pbar.write() to prevent terminal flickering/staircasing. 5. Dependency Updates Added tqdm as a core dependency for enhanced console visualization.	2 months ago
RUSHIL AMBARISH KADU	ff43a9efb6	Upgrade Ground Truth Schema (Critical for ADAS use-case) Expanded object state to include physical (acceleration), geometric (bounding boxes), and relative ADAS metrics (range, azimuth, closing velocity) for vehicles and pedestrians.	2 months ago
RUSHIL AMBARISH KADU	2d6d04e0b0	feat: initial commit CARLA ADAS modular scenario-driven simulation pipeline ## Summary First commit of the Fox CARLA simulation pipeline. Establishes a fully working, end-to-end ADAS data collection and visualization system built on CARLA 0.9.16. --- ## What is included ### Core Pipeline - Multi-sensor synchronous simulation (Camera, Radar, LiDAR) at configurable FPS - Frame-aligned sensor data captured using CARLA synchronous mode with fixed delta time - Structured dataset output: PNG images, NPY arrays, JSONL metadata per frame - MCAP conversion script producing Foxglove-compatible topics (/camera, /lidar, /radar, /ego_pose) - Ground truth vehicle tracking embedded in every frame record ### Modular Scenario Architecture - Abstract ScenarioBase plugin interface (setup / step / cleanup lifecycle) - Dynamic scenario loader via importlib main.py never imports scenarios by name - Three implemented ADAS test scenarios: - braking : Lead vehicle hard braking (emergency stop at configurable frame) - cutin : Adjacent lane NPC forced lane change into ego lane - obstacle : Static traffic cone placed on ego lane - All NPC placement via CARLA waypoints zero hardcoded world coordinates - Scenario metadata embedded into every JSONL frame record for MCAP traceability ### Orchestrator (src/main.py) - Fully scenario-agnostic scenarios selected via --scenario CLI flag - Supports --frames override for quick test runs without editing config - Supports --list-scenarios for dry-run discovery (no CARLA server required) - Deferred CARLA import so the process works without a live server for non-simulation commands ### Configuration (config.py) - Single source of truth for all simulation, sensor, and scenario constants - Scenario trigger frames and distances fully configurable without code changes ### Recorder (src/recorder.py) - Session folders named <scenario>_YYYYMMDD_HHMMSS for easy identification - extra_meta parameter merges scenario state into every frame record automatically ### Developer Tooling - run.bat: one-click launcher that activates the carla312 conda environment - intel/context.md: comprehensive AI-agent and developer reference document - .gitignore: excludes data/, .mcap, __pycache__, logs/, .pyc --- ## Architecture src/main.py (orchestrator) \|-- scenario_loader.py (importlib-based dynamic loader) \|-- sensors.py (SensorManager: camera + radar + lidar) \|-- recorder.py (per-frame data writer) scenarios/ \|-- base.py (ScenarioBase ABC) \|-- braking.py (BrakingScenario) \|-- cutin.py (CutInScenario) \|-- obstacle.py (ObstacleScenario) config.py (all constants) data_to_mcap.py (dataset -> MCAP converter) run.bat (conda env launcher) intel/context.md (codebase reference) --- ## Usage run.bat braking run.bat cutin --frames 120 run.bat obstacle python data_to_mcap.py --- ## Environment - CARLA 0.9.16 - Python conda env: carla312 (miniconda) - Key deps: carla, numpy, opencv-python, mcap --- ## Known Limitations (future work) - MCAP uses JSON encoding (functional; Protobuf migration planned) - Intersection scenario not yet implemented (requires junction-aware waypoint logic) - Single ego vehicle assumed throughout - Foxglove layout presets not yet defined	2 months ago