Simulator

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Author	SHA1	Message	Date
RUSHIL AMBARISH KADU	7089c35d09	refactor: generic orchestrator with CLI param injection - Added --params CLI flag for dynamic scenario tuning - Implemented orchestration priority chain: CLI > Scenario > Global Config - Added world.tick() before setup to ensure physics synchronization - Implemented robust ego 'Spawn-and-Move' strategy for absolute coordinates - Cleaned up config.py from scenario-specific constants	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	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

7089c35d09

refactor: generic orchestrator with CLI param injection

- Added --params CLI flag for dynamic scenario tuning
- Implemented orchestration priority chain: CLI > Scenario > Global Config
- Added world.tick() before setup to ensure physics synchronization
- Implemented robust ego 'Spawn-and-Move' strategy for absolute coordinates
- Cleaned up config.py from scenario-specific constants

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

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 (c568c111063d9161b4d26e088b0c0d134216a8d3)