Simulator

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Commit Graph

Author	SHA1	Message	Date
RUSHIL AMBARISH KADU	78205ad32a	feat: Aligned simulation pipeline with high-fidelity testbench diagnostics - Upgraded run.bat with robust argument logic and unbuffered logging for Dashboard SSE. - Integrated multi-radar support (awrl1432, radarbook) in generate_shenron.py. - Unified MCAP topic naming convention (/radar/native, /radar/{type}/metrics, /radar/{type}/heatmaps). - Added per-radar telemetry generation and packaging using foxglove.Telemetry schema. - Modernized heatmap colormap API and added numerical safety clips.	1 month 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	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

Author

SHA1

Message

Date

RUSHIL AMBARISH KADU

78205ad32a

feat: Aligned simulation pipeline with high-fidelity testbench diagnostics

- Upgraded run.bat with robust argument logic and unbuffered logging for Dashboard SSE.
- Integrated multi-radar support (awrl1432, radarbook) in generate_shenron.py.
- Unified MCAP topic naming convention (/radar/native, /radar/{type}/metrics, /radar/{type}/heatmaps).
- Added per-radar telemetry generation and packaging using foxglove.Telemetry schema.
- Modernized heatmap colormap API and added numerical safety clips.

1 month 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

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

3 Commits (c37f45e9b3dbff3049b5570150c7d0d8652ad28e)