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 represents a major architectural upgrade to the CARLA ADAS simulation project, transitioning from stochastic Traffic Manager behavior to a fully deterministic, high-impact demo environment.
1. Deterministic Scenario Architecture
New Showcase Scenario: Implemented
scenarios/showcase.py
for a manual "Left-Turn Across Path" (LTAP) maneuver.
Physics Velocity Injection: Overrode engine/friction variance by directly setting set_target_velocity for NPC actors at 35 km/h.
Multi-Stage Waypoint Guidance: Integrated EGO_MID and P1_MID transition points to ensure realistic, cinematic turning radii and reliable collision-avoidance timing.
Manual Steering Controller: Developed a proportional steering-to-target helper for all actors.
2. Sensor & Data Pipeline
Dual-Camera Support: Integrated a second Third-Person Perspective (TPP) camera.
Radar Serialization Fix: Implemented trigonometric conversion of raw spherical coordinates (Depth, Azimuth, Altitude) into Cartesian XYZ + Velocity 16-byte PointClouds for Foxglove Studio.
Synchronization: Aligned all sensor data (Dash Cam, TPP Cam, Radar, Lidar) at the orchestrator level.
3. Performance & Asynchronous I/O
Asynchronous Recorder: Refactored
src/recorder.py
to use ThreadPoolExecutor, offloading cv2.imwrite operations to background threads to eliminate simulation stutter.
Rapid Iteration: Added --no-record global flag to
main.py
for sub-second iteration during coordinate tuning.
4. Automation & Seamless UX
Auto-MCAP conversion: Integrated
data_to_mcap.py
as a post-simulation callback in
main.py
.
Auto-Video Stitching: Implement automatic .mp4 generation for both camera views using OpenCV's VideoWriter.
Weather/Time CLI: Added --weather flag with presets (Clear, Rain, Sunset, Night).
Console Sync: Integrated tqdm for professional progress tracking and refactored scenario logging to use pbar.write() to prevent terminal flickering/staircasing.
5. Dependency Updates
Added tqdm as a core dependency for enhanced console visualization.
RUSHIL AMBARISH KADU