Summary:
Added comprehensive documentation and environment setup files to ensure
seamless project replication on different machines. This includes a
centralized README.md and standardized dependency manifests for both
pip and conda.
Changes:
- Created README.md: A complete source of truth for the Fox CARLA ADAS
Simulation project, covering prerequisites, multi-stage pipeline
architecture, and detailed installation steps.
- Created requirements.txt: A curated list of project dependencies
(PyTorch, Flask, MCAP, OpenCV, etc.) extracted from the active
carla312 environment.
- Created environment.yml: A Conda environment specification file
for one-command environment replication including Python 3.12
and the necessary scientific stack.
- Added Troubleshooting section to README covering GPU Idle Mode,
stop.flag detection, and CARLA coordinate system conventions.
Technical Note:
The setup instructions explicitly detail the manual installation of the
CARLA 0.9.16 .whl file, as it is a local dependency relative to the
project root within the CARLA PythonAPI directory.
This release marks a major architectural shift and achieves full feature
parity with the high-fidelity Shenron radar testbench.
Architecture:
- Implemented PipelineManager and Stage-based execution (Sim, Shenron, MCAP, Video).
- Decoupled recorder from physics/augmentation logic into src/processing/physics.py.
- Migrated all path resolution to Pathlib for cross-platform robustness.
Radar & Metrology:
- Integrated FastHeatmapEngine for stateful, high-performance RD/RA/CFAR rendering.
- Implemented dual-plot Range-Azimuth (Static Absolute vs Dynamic Peak).
- Flattened telemetry schema for direct Foxglove Plot panel compatibility.
- Added hardware-accurate 3D FOV frustum projections (LINE_LIST).
Dashboard & UX:
- Bumped version to v1.1.
- Fixed stop.flag detection bug in simulation shutdown.
- Optimized GPU headroom by maintaining idle/sync mode during synthesis.
Signed-off-by: Antigravity AI <cortex@deepmind.google>
- Refactored heatmap rendering, stateful engine, and scan conversion from test_shenron.py into new sim_radar_utils.plots module
- Updated FastHeatmapEngine to automatically respect xRange/yRange overrides sourced from config.yaml
- Calibrated Range-Doppler plot limits to ±8 m/s in config.yaml for optimized visual inspection
- Cleaned up test_shenron.py by removing ~150 lines of redundant visualization logic and heavy Matplotlib imports
- Added __init__.py to sim_radar_utils to ensure proper package identification
- physics: increased metal roughness in Sceneset.py to 1.5mm for better diffuse scattering
- cfar: widened guard cells to 5x5 in config.yaml to isolate clutter; threshold kept at 20dB
- signal-processing: implemented 3x3 local maximum filter (NMS) in radar_processor.py to eliminate sidelobes
- config: enabled peak_grouping toggle in config.yaml
This physics engine overhaul introduces two major fidelity upgrades to the Shenron pipeline: 1. Physical Area Integration: Tethers incident power natively to the solid angle expanding physical area (dTheta*dPhi*R^2) ensuring cross-section energy output scales independently of LiDAR point cloud resolution. 2. Millimeter Jitter: Maps physical property roughness (e.g., 5mm concrete, 50um metal) as a sub-wavelength Gaussian distance distorter. This fractures the 'perfect mathematical mirror' bug inherent to flat CARLA meshes by substituting naive coherent summation (N^2) with physical diffuse Rayleigh scattering (N). Includes addition of interactive 3-slide HTML and dark-mode simulation presentation decks for project reporting.
Transitioned the Shenron radar engine from a rigid architecture to a modular,
tunable "Knobs and Dials" framework. This update establishes a physics-first
baseline derived from real-world electromagnetic behavior.
Core Changes:
- Modular Radar Profiles: Pre-configured profiles for TI Cascade, Radarbook,
and AWRL1432 with hardware-specific Bandwidth, Chirp, and nRx parameters.
- Physics Core (Sceneset.py):
- Full implementation of Fresnel and Beckmann-Spizzichino scattering.
- Pure 1/R^4 power law (1/R^2 transmit, 1/R^2 receive) via legacy scaling removal.
- Fixed cos(cos(theta)) bug in CARLA semantic lidar mapping.
- Antenna Gain Integration:
- Implemented separable Azimuth/Elevation gain patterns.
- Added Symmetric Azimuth LUT interpolation and Vertical FOV Hard Cutoff.
- Signal Processing:
- Optimized GPU-accelerated signal synthesis using PyTorch.
- Standardized 110 dB System Calibration Constant for hardware SNR matching.
Architecture & Documentation (intel/radar):
- Reorganized radar intel directory into structured subfolders (core, research,
diagnostics, archive) for better scalability.
- Added SHENRON_MODULAR_ARCHITECTURE.md (System overview).
- Added SHENRON_ANTENNA_GAIN_CALIBRATION.md (Gain physics deep-dive).
- Modernized package README with Windows/Conda specific usage instructions.
- Synchronized all internal and external documentation links.
Calibration: Iteration 18 (Antenna Gain Baseline)
Note: Remaining magic numbers in get_loss_3 are noted for future migration.
Transitioned the Shenron radar engine from a rigid architecture to a modular,
tunable "Knobs and Dials" framework. This update establishes a physics-first
baseline derived from real-world electromagnetic behavior.
Core Changes:
- Modular Radar Profiles: Pre-configured profiles for TI Cascade, Radarbook,
and AWRL1432 with hardware-specific Bandwidth, Chirp, and nRx parameters.
- Physics Core (Sceneset.py):
- Full implementation of Fresnel and Beckmann-Spizzichino scattering.
- Pure 1/R^4 power law (1/R^2 transmit, 1/R^2 receive) via legacy scaling removal.
- Fixed cos(cos(theta)) bug in CARLA semantic lidar mapping.
- Antenna Gain Integration:
- Implemented separable Azimuth/Elevation gain patterns.
- Added Symmetric Azimuth LUT interpolation and Vertical FOV Hard Cutoff.
- Signal Processing:
- Optimized GPU-accelerated signal synthesis using PyTorch.
- Standardized 110 dB System Calibration Constant for hardware SNR matching.
Additional Documentation:
- intel/radar/SHENRON_MODULAR_ARCHITECTURE.md: Architecture and "Knobs/Dials" overview.
- intel/radar/SHENRON_ANTENNA_GAIN_CALIBRATION.md: Physics of Antenna Gain and 1/R^4 logic.
Note: Remaining magic numbers in get_loss_3 (K_sq, scat_normalization, lobe_frac)
are noted for future migration into ConfigureRadar.py.
- Fix: Implement Heartbeat Tick in dashboard backend to prevent CARLA window from freezing during GPU Idle Mode.
- Feat: Add "Force Kill Simulator" functionality to terminate unresponsive engine instances.
- Feat: Add "Nuclear EXIT ALL" button to sidebar for coordinated shutdown of CARLA, Flask server, and Browser.
- UI/UX: Design and center a premium "System Offline" overlay for graceful exit visibility.
- Automation: Implement auto-minimization logic in dashboard.bat to keep the workspace clean.
- Optimized dashboard polling (3s) and suppressed noisy terminal status logs.
- Implemented collapsible 'Configuration Parameters' card with localStorage persistence.
- Overhauled dashboard theme for high-contrast accessibility and element visibility.
- Initialized intel/CHRONICLES.md to document project saga and technical breakthroughs.
- Established intel/memory_update.md protocol for automated repository memory updates.
Standardizes the metrology diagnostic suite for high-fidelity focus and
consistent dB scaling.
Core Improvements:
- Signal Processing: Switched Range and Doppler FFTs to Hann windowing,
reducing peak width by ~50% for sharper target focus (Iteration 27).
- Visualization Style: Implemented a fixed-scale MATLAB-style Jet colormap
(-5 to 45 dB). Disabled per-frame auto-scaling to preserve temporal
intensity consistency and physical R^-4 decay.
- Calibration: Applied a -68.0 dB System Gain Offset in post-processing
(calculated via statistical analysis of raw power levels in Iter 28)
to align noise floors (~7 dB) and target peaks (~41 dB).
- Pipeline: Standardized these focus and calibration settings across both
the testbench (test_shenron.py) and packaging pipeline (data_to_mcap.py).
- Docs: Verified and restored gemini.md at the repository root as the
master project context.
This milestone (Iteration 29) finalizes the visual interpretability of the
physics-based Shenron radar engine.
Restores high-fidelity physical realism to the C-Shenron radar engine by
aligning the synthesis pipeline with the pure Radar Range Equation.
Core Improvements:
- Physics: Restored 1/R^4 power-delay law (1/R^2 voltage) in Sceneset.py
and heatmap_gen_fast.py. Stripped legacy 1/1000 normalizations and
R^2 area-growth workarounds.
- Geometry: Fixed FFT index asymmetry in radar_processor.py, achieving a
perfectly symmetric 120° FOV sector.
- Metrology: Implemented "Radar Blue" (Viridis) 120° fan-projection for
diagnostic Range-Azimuth heatmaps.
- Automation: Integrated RD/RA/CFAR heatmap persistence into the
automated simulation-to-MCAP pipeline (data_to_mcap.py).
- Docs: Comprehensive update of intel/ directory, including Iterations 17-26
and the Physics/Symmetry Milestone deep-dive.
This milestone ensures that target brightness and spatial positioning
correctly mimic real-world TI AWRL1432 radar hardware.
- Engine: Modified CFAR and Processor to extract raw Range-Doppler and Range-Azimuth energy heatmaps.
- Visuals: Integrated high-fidelity Matplotlib colormapping (Viridis/Magma) for Foxglove image streaming.
- Data: Implemented 32-bit raw .npy persistence and JSONL telemetry for frame-level SNR analysis.
- Tools: Added a dedicated verification utility for end-to-end signal flow validation.
- Docs: Comprehensive documentation for the new 'Radar Lab' architecture in /intel/.
Implemented a major architectural refactor of the C-SHENRON radar engine to achieve physically stable, context-independent detections.
1. Physics Engine Refactor (Iteration 16):
- Sceneset.py: Replaced global 1/N normalization with a fixed Area-Density Integration model. This prevents Energy Starvation where buildings previously suppressed car detections.
- Gaussian Damping: Integrated a 20 deg vertical beamwidth profile to physically suppress tree-top clutter while preserving boresight targets.
- ConfigureRadar.py: Standardized hardware profiles with calibrated 110dB gain and removed Iteration 15 bandages.
2. Repository Reorganization:
- Moved analysis and verification scripts to a new scripts/analysis/ directory.
- Updated path resolution in moved scripts to ensure project-wide stability.
3. Documentation & Metrology:
- Created 3D vertical energy suppression.md detailing the Resolution Independence breakthrough.
- Updated Shenron_debug.md with Iterations 14-16, recording a +234% Target Magnitude recovery.
- Updated Shenron_Debug_Plan.md and possible_issue_resolution.md to reflect resolved technical blockers.
Implemented Gaussian vertical-gain damping (-2.77 constant) to address the 'Super-Reflector' tree density problem.
- [ConfigureRadar] Defined total vertical_beamwidth for AWRL1432 (30.0) and Radarbook (60.0).
- [Sceneset] Added G_vertical damping math in get_loss_3 to suppress high-elevation energy.
- [Sceneset] Retained debug logs (G_vertical average/min/max) for Iteration 14b calibration.
Hiccups:
- Resolved environment activation (carla312) in verification shell.
- Corrected test_shenron.py command syntax.
This commit marks the completion of the Iteration 13 'Golden Mix' calibration.
- intel/: Structured documentation into /radar, /scenarios, and /internal subfolders.
- Shenron_debug.md: Comprehensive history of all 13 calibration iterations.
- radar_processor.py: Implemented Blackman-Harris windowing for sidelobe suppression.
- lidar.py: Applied -2.0m parallax shift and ground suppression filtering.
- Sceneset.py: Calibrated specular thresholds and material roughness for stability.
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.
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.
