CARLA ? C-Shenron based Simualtor for Sensor data generation.
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<title>Fox CARLA ADAS Simulation Platform</title>
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<!-- Slide 1: Title -->
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<div class="company-logo">FOX <span style="color:var(--text-main);">ADAS</span></div>
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<h1>High-Fidelity Deterministic<br><span class="highlight">Radar Simulation</span></h1>
<p>Moving from bounding-box emulation to mathematically pure, hardware-configurable physics validating the Shenron Pipeline.</p>
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<!-- Slide 2: The Vision & Physical Approach -->
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<h2>The Physical Foundation: 1/R⁴ Physics</h2>
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<p>Standard simulator bounding-boxes bypass the complex realities of electromagnetic propagation. The Shenron engine enforces strict free-space physics for absolute accuracy.</p>
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<h3><i class="card-icon fa-solid fa-bolt"></i> Milestone R4: Pure Physics</h3>
<p>We abandoned artificial density and distance normalizations. All generated scatterers now strictly follow the <span class="bold highlight">Radar Range Equation (1/R⁴)</span>. A building at distance naturally drops in power compared to a vehicle up close.</p>
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<h3><i class="card-icon fa-solid fa-car-burst"></i> Deterministic Coordinate Control</h3>
<p>Standard Traffic Manager leads to high variance. Using a <span class="bold">Spawn-and-Move</span> logic with enforced Velocity overrides guarantees complex scenarios (like left-turn cut-ins) occur at the exact same millisecond every run.</p>
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<div class="title">Insert Screenshot: 1/R⁴ Contrast</div>
<div class="instructions">Provide a side-by-side or CFAR output showing how distant heavy clutter is naturally attenuated while the closer vehicle is correctly highlighted due to the inverse square law path loss.</div>
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<h2>Unprecedented Configurability: "Knobs & Dials"</h2>
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<p>The Shenron architecture eliminates hardcoded physical scalars. Instead, it exposes a "Control Panel" mimicking real hardware tuning. <span class="bold">If we do it on the test track, we can do it in the simulation.</span></p>
<h3>Supported Hardware Profiles</h3>
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<span class="tag">TI AWRL1432 (ADAS)</span>
<span class="tag">Radarbook (Research)</span>
<span class="tag">TI Cascade (MIMO)</span>
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<ul style="margin-top: 30px;">
<li><span class="highlight">Bandwidth (B) & Chirps:</span> Accurately adjust range and doppler resolution directly reflecting radar specifications.</li>
<li><span class="highlight">System Gain Calibration (110dB):</span> Modify the global signal chain gain ($P_t G_t G_r$) without touching the core physics engine.</li>
<li><span class="highlight">Antenna Arrays (nRx/nTx):</span> Instantly swap between 6 virtual receivers (1432) or 8 virtual receivers (Radarbook) to measure DSP beamforming limits.</li>
<li><span class="highlight">Noise Amplitude:</span> Dynamically inject AWGN thermal noise to test CFAR floor robustness.</li>
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<div class="title">Insert Code/UI Screenshot: Customizing Radar</div>
<div class="instructions">Please insert a screenshot showing `ConfigureRadar.py` or the specific JSON payload being passed via the dashboard to demonstrate how easily we swap between a 1432 profile and a Radarbook profile.</div>
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<!-- Slide 4: Deep Observability & Metrology -->
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<h2>Deep Observability: The Metrology Suite</h2>
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<p>Instead of treating synthetic radar output as a "black box," our new pipeline visualizes the entire signal chain. This allows engineers to cross-validate ADC output against physical ground truth.</p>
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<h4>The "Radar Blue" Standard</h4>
<p>We instituted phase-preserving <span class="bold">Doppler-Slice Synthesis</span> to replace artifact-heavy coherent integration, resulting in pristine, sharp heatmaps.</p>
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<li><span class="bold">Range-Doppler (RD):</span> Extracts target separation and velocity, preventing velocity zeroing.</li>
<li><span class="bold">Range-Azimuth (RA):</span> Confirms azimuthal accuracy utilizing Zero-Tilt Symmetry (120° FOV perfectly aligned to boresight).</li>
<li><span class="bold">CFAR Gate:</span> Visualizes adaptive thresholds distinguishing targets from the synthesized noise floor.</li>
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<div class="title">Insert Screenshot: 3-Plot Metrology Suite</div>
<div class="instructions">Insert a screenshot of the Foxglove layout showing the 3 core metrology graphs (RD, RA, and CFAR) running alongside the 3D Pointcloud view to illustrate deep observability.</div>
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<!-- Slide 5: Major Breakthroughs & Bug Squashing Engine -->
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<h2>Major Breakthroughs & Bug Squashing Engine</h2>
<p>Achieving this level of physical simulation required identifying and tearing down several legacy miscalculations that corrupted early models.</p>
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<h3><i class="fa-solid fa-satellite-dish"></i> The Isotropic Illumination Fix</h3>
<p>The deepest physics problem resolved. Early simulation allowed side-targets at 80° to return 100% power, masking forward vehicles. <br><br>We implemented <span class="highlight">Horizontal (Azimuth) & Vertical (Elevation) Directional Gain Patterns</span>. Clutter at wide angles is now correctly attenuated by ~15-20 dB, letting the true target dominate.</p>
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<h4><i class="fa-solid fa-bug" style="color:var(--color-danger); margin-right:8px;"></i> The Cos(Cos) Reflection Error</h4>
<p style="font-size:22px; margin-bottom:0;">Recovered 46% of lost signal energy by tracking down a double-cosine application in the Open3D coordinate translation pipeline.</p>
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<h4><i class="fa-solid fa-bug" style="color:var(--color-danger); margin-right:8px;"></i> Floating Point Data Packing</h4>
<p style="font-size:22px; margin-bottom:0;">Resolved the infamous "0.0 velocity" bug by utilizing `np.view(np.uint32)` to correctly deserialize perfectly packed 32-bit semantic tags.</p>
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<!-- Slide 6: The Full Automation Pipeline -->
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<h2>The Full Automation Pipeline</h2>
<p>A completely automated workflow enabling one-click testbenches, fully orchestrated via a web dashboard.</p>
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<div class="title">Insert Graphic/Screenshot: Pipeline & Dashboard</div>
<div class="instructions" style="color:var(--text-main);">
Place a diagram or screenshot here showing the flow:<br><br>
<b>1. GUI Dashboard (with Hardware GPU Idle Mode)</b> &rarr; <b>2. CARLA Simulator</b> &rarr; <b>3. Shenron Physics</b> &rarr; <b>4. MCAP Serializer</b> &rarr; <b>5. Foxglove Studio</b>
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<p>From deterministic scenario configuration to a 3D Foxglove validation pointcloud — without manual intervention.</p>
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