└── internal/ ← Project-wide context and developer guides
└── context.md ← This file (Primary entry point)
```
```
---
---
@ -71,7 +75,7 @@ Fox/
### `dashboard.bat`&`dashboard/` — Web GUI Orchestrator
### `dashboard.bat`&`dashboard/` — Web GUI Orchestrator
A Flask-based web dashboard that provides an intuitive interface for running CARLA scenarios without the CLI. It dynamically fetches available scenarios and config params, translates user choices into `run.bat` commands as a background subprocess, and heavily streams the unbuffered Python stdout text back to the browser using Server-Sent Events (SSE).
A Flask-based web dashboard that provides an intuitive interface for running CARLA scenarios without the CLI. It dynamically fetches available scenarios and config params, translates user choices into `run.bat` commands as a background subprocess, and heavily streams the unbuffered Python stdout text back to the browser using Server-Sent Events (SSE).
> **Full Architecture details:** See `intel/dashboard.md` for a complete breakdown of API routing and extension guidelines.
> **Full Architecture details:** See `intel/scenarios/dashboard.md` for a complete breakdown of API routing and extension guidelines.
---
---
@ -334,4 +338,15 @@ class MyScenario(ScenarioBase):
**Objective:** Resolve physics-based discrepancies in the C-Shenron synthetic radar pipeline, align with AWRL1432BOOST hardware performance, and establish a stable, high-fidelity simulation baseline.
**Objective:** Resolve physics-based discrepancies in the C-Shenron synthetic radar pipeline, calibrate physical materials for turn-stability, and align sensor coordinate frames for zero-parallax synchronization.
**Root Cause:** The semantic lookup table was using legacy CARLA 0.9.5 tags. In CARLA 0.9.16, NPC vehicles moved to **Tag 14**, and Terrain moved to **Tag 10**.
**Effect:** All NPC cars were being deleted from the point cloud (filtered out as "unknown"), while the ground terrain was being incorrectly simulated as a high-RCS "Metal" object, creating massive false alarms.
**Fix:** Updated the `map_carla_semantic_lidar_latest` mapping to align with modern 0.9.16 standards.
---
## 📡 Hardware Profile: AWRL1432 (Iteration 07 — Current Best)
## 📡 Hardware Profile: AWRL1432 (Iteration 07 — Current Best)
The `awrl1432` profile in `ConfigureRadar.py` has been tuned to match the real-world **TI AWRL1432BOOST** professional ADAS configuration:
The `awrl1432` profile in `ConfigureRadar.py` has been tuned to match the real-world **TI AWRL1432BOOST** professional ADAS configuration:
@ -76,11 +83,48 @@ The `awrl1432` profile in `ConfigureRadar.py` has been tuned to match the real-w
1. **Turn Lag:** Shenron points appear to trail ~0.5-1 frame behind the CARLA native radar during sharp turns. Suspected cause: LiDAR data captured at `T-1` but rendered with ego pose at `T`. Requires timestamp sync investigation in `recorder.py`.
1. **Turn Lag:** Shenron points appear to trail ~0.5-1 frame behind the CARLA native radar during sharp turns. Suspected cause: LiDAR data captured at `T-1` but rendered with ego pose at `T`. Requires timestamp sync investigation in `recorder.py`.
2. **Angular FOV Validation:** Compare Shenron angular output vs. AWRL1432BOOST hardware spec (`+/- 60°`) to ensure angular clipping is not removing valid detections.
2. **Angular FOV Validation:** Compare Shenron angular output vs. AWRL1432BOOST hardware spec (`+/- 60°`) to ensure angular clipping is not removing valid detections.
3. **CFAR Threshold Tuning:** The `threshold: 20` in `config.yaml` may need adjustment after the noise floor restoration. Consider running a "Clear Road" baseline to calibrate the false alarm rate.
3. **CFAR Threshold Tuning:** The `threshold: 20` in `config.yaml` may need adjustment after the noise floor restoration. Consider running a "Clear Road" baseline to calibrate the false alarm rate.
4. **3D Energy Compression (Tree Density):** Current logic flatten 10m of vertical tree volume into a single 2D bin, making trees look 10x "louder" than cars. **Proposed Fix:** Implement a Gaussian Vertical Beam Pattern in `Sceneset.py` to dampen high-elevation points.
