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4 Commits (bf935c4d15e80f7bf5e93c6eb8bfe17e347e283a)

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	2419e06517	feat(radar): restore physical 1/R^4 path loss and symmetric 120° FOV 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.	1 month ago
RUSHIL AMBARISH KADU	8ec04b881c	feat(shenron): Integrate C-SHENRON physics-based radar simulation - Transitions simulation to Model-Based Development (MBD) architecture. - Added semantic LiDAR sensor with high-fidelity point cloud recording. - Fixed '0 points' bug by implementing bit-casting for uint32 semantic tags (Tag 10 -> Metal). - Corrected antenna count hardcoding and range-slicing errors in radar processor. - Synchronized hardware configs between project settings and simulation engine. - Automated synthesis and MCAP serialization in src/main.py cleanup phase. - Verified rich 5-field point cloud output (x, y, z, velocity, magnitude) in showcase.	2 months ago
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