- 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/.
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.
RUSHIL AMBARISH KADU