PyRoboVision — Complete Autonomous Driving Stack (v1.2 v2.0)

Vision model intelligence platform. Track, evaluate, and improve computer vision models with performance validation and best-model identification.

PyRoboVision combines real-time multi-object tracking (v1.2), 3D perception fusion (v1.5), and safety-constrained learning with GPU optimization (v2.0) into a modular, production-ready framework. Built for robotics and autonomous vehicles with quality validation at every stage.

Architectural Role: Owns vision model registry and performance tracking. Central repository for model performance, enables model selection based on quality + cost + latency tradeoffs. Part of ML operations foundation.

Key differentiator: PyRoboVision bridges the gap between perception and learning in a single unified pipeline — tracking objects, predicting trajectories, estimating depth, fusing sensors, and training safe policies end-to-end. Modular design lets you swap components without retraining.

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What's Inside

v1.2: Tracking & Prediction (August 2026)

• Multi-object tracking (MOT) — Kalman filter + Hungarian algorithm association
• Trajectory prediction — Constant velocity/acceleration models with uncertainty
• Behavioral analysis — 8-class motion classification (stopped, turning, accelerating, etc.)
• Intent prediction — 11-class intent forecasting (lane change, acceleration, collision avoidance)
• Real-time performance — <50ms per frame at 480p, <2ms latency

v1.5: 3D Perception (September 2026)

• Monocular depth estimation — Single-image depth with LiDAR fusion
• 3D bounding boxes — Depth-to-3D conversion with PCA-based orientation
• Occupancy grids — Bird's-eye-view representation for planning
• LiDAR processing — Point cloud filtering, clustering, normal estimation
• Multi-sensor fusion — Depth-LiDAR fusion for accuracy improvement

v2.0: End-to-End Learning & Optimization (October 2026)

• Imitation learning — Demonstration collection + trajectory augmentation
• Behavior cloning — Supervised learning from expert demonstrations
• Policy networks — Actor-critic with safety constraints
• Safety validation — Constraint-based action correction (acceleration, steering, collision)
• Training infrastructure — Early stopping, checkpointing, convergence analysis
• Sensor fusion — IMU/GPS Kalman fusion with automatic coordinate transformation
• Model optimization — ONNX export, TensorRT compilation, int8/int16/float16 quantization, inference profiling

v1.1: Foundation Models & Perception

• Cylindrical panoramic stitching — 360 multi-camera fusion (Waymo, nuScenes)
• Advanced blending — Laplacian pyramid + graph-cut seams
• Bird's-eye-view (BEV) — 3D projection for autonomous perception
• GPU acceleration — CuPy (NVIDIA), MLX (Apple Silicon), NumPy (CPU)
• Sensor fusion — Lidar/Radar + occupancy grid mapping
• SAM3 segmentation — Instance segmentation + temporal tracking
• CLIP embeddings — Scene understanding, text-image similarity
• Grounding DINO — Open-vocabulary object detection

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Installation

# Requires PyRoboFrames 1.1.0+
pip install "pyroboframes>=1.1.0" pyrobovision

# With NVIDIA GPU support
pip install "pyroboframes>=1.1.0" "pyrobovision[cuda]"

# With Apple Silicon (MLX)
pip install "pyroboframes>=1.1.0" "pyrobovision[mlx]"

# From source
git clone https://github.com/Mullassery/PyRoboVision.git
cd PyRoboVision
pip install -e .

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Quick Start

v1.2: Real-Time Multi-Object Tracking & Intent Prediction

from pyrobovision.tracking.mot import MOTTracker, Detection
from pyrobovision.intent.predictor import IntentPredictor
import numpy as np

# Initialize tracker
tracker = MOTTracker(max_age=30, min_hits=3)
intent_predictor = IntentPredictor(lookahead_frames=30)

# Process video frame-by-frame
for frame_id in range(num_frames):
 # Get detections from your detector (YOLO, SAM2, etc.)
 detections = [
 Detection(bbox=np.array([x, y, x+w, y+h]), confidence=0.95)
 for x, y, w, h in detected_objects
 ]
 
 # Track objects across frames
 confirmed_tracks = tracker.update(detections)
 
 # Predict intent for each tracked object
 for track in confirmed_tracks:
 positions = np.array([d.bbox[:2] for _, d in track.detections])
 velocities = np.diff(positions, axis=0)
 
 intent = intent_predictor.predict_intent(positions, velocities, ...)
 print(f"Track {track.track_id}: {intent.intent.value} (confidence: {intent.confidence:.2f})")

v1.5: 3D Perception with Depth-LiDAR Fusion

from pyrobovision.perception.depth import DepthEstimator
from pyrobovision.perception.bbox_3d import Box3DConverter
from pyrobovision.perception.occupancy import OccupancyGridBuilder

# Estimate depth from single RGB image
estimator = DepthEstimator(model="midas")
estimator.set_calibration(fx=500, fy=500, cx=320, cy=240)

rgb_frame = ... # Your camera frame
depth_map = estimator.estimate_depth(rgb_frame)

# Convert 2D detections to 3D bounding boxes
converter = Box3DConverter()
bbox_3d = converter.from_2d_bbox_and_depth(
 bbox_2d=np.array([100, 100, 200, 200]),
 depth_map=depth_map.data,
 fx=500, fy=500, cx=320, cy=240
)

# Generate occupancy grid for planning
builder = OccupancyGridBuilder(grid_size=(100, 100), resolution=0.1)
occupancy_grid = builder.from_3d_bboxes([bbox_3d])
print(f"Occupied cells: {len(occupancy_grid.get_occupied_cells())}")

v2.0: End-to-End Learning with Safety Constraints & Sensor Fusion

from pyrobovision.learning.imitation import ImitationLearner
from pyrobovision.learning.behavior_cloning import BehaviorCloningModel
from pyrobovision.learning.safety import SafetyValidator
from pyrobovision.learning.training import TrainingConfig, Trainer
from pyrobovision.fusion.sensor_fusion import SensorFusionEngine, IMUData, GPSData
from pyrobovision.fusion.optimization import ModelOptimizer, QuantizationConfig

# Multi-sensor fusion: IMU + GPS
fusion_engine = SensorFusionEngine(origin_lat=37.7749, origin_lon=-122.4194)

imu_data = IMUData(timestamp=0.0, accelerometer=[0, 0, 9.8], gyroscope=[0, 0, 0])
state = fusion_engine.update_imu(imu_data)

gps_data = GPSData(latitude=37.7749, longitude=-122.4194, altitude=10.0,
 speed=5.0, heading=90.0, accuracy=2.0)
state = fusion_engine.update_gps(gps_data)
print(f"Fused position: {state.position}, uncertainty: {state.covariance}")

# Collect expert demonstrations and train
learner = ImitationLearner(obs_dim=8, action_dim=2)
for episode in expert_trajectories:
 for obs, action, next_obs, reward in episode:
 learner.record_transition(obs, action, next_obs, reward, done=False)

model = BehaviorCloningModel(obs_dim=8, action_dim=2)
config = TrainingConfig(obs_dim=8, action_dim=2, num_epochs=10)
trainer = Trainer(config)
trainer.train(model, train_observations, train_actions)

# Validate with safety constraints
validator = SafetyValidator(max_acceleration=5.0, max_steering=45.0)
action = model.predict(obs, deterministic=True)
safe_action = validator.correct_action(action, {"speed": 15.0})

# Optimize for production deployment
optimizer = ModelOptimizer(model=model, device="gpu")
optimizer.export_to_onnx("model.onnx", example_input)
optimizer.export_to_tensorrt("model.onnx", "model.trt", max_batch_size=8)

config = QuantizationConfig(quantization_type="int8", per_channel=True)
optimizer.quantize_model(config)

stats = optimizer.profile_inference(test_input, num_iterations=100)
print(f"Inference latency: {stats['p95_latency_ms']:.2f}ms @ p95")

v1.1: Foundation Models & Panoramic Perception

from pyrobovision.automotive import CylindricalStitcher, get_waymo_layout
from pyrobovision.foundation_models import MultiModalFusion

# 360 panoramic stitching
layout = get_waymo_layout()
stitcher = CylindricalStitcher(layout, blend_method="laplacian")

frames = {"FRONT": ..., "FRONT_LEFT": ..., ...}
panorama = stitcher.stitch(frames)

# Multi-modal scene understanding
fusion = MultiModalFusion(
 detection_prompt="car . pedestrian . cyclist",
 device="mlx",
)

scene = fusion.understand(panorama)
for obj in scene.objects:
 print(f"{obj.object_class}: {obj.semantic_label}")

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Architecture

Dependency Graph

PyRoboVision/
 automotive/ # v0.5 AV perception
 stitching.py
 blending.py
 bev.py
 perception_3d.py
 tfrecord_utils.py
 nuscenes_utils.py
 datasets.py

 foundation_models/ # Phase 7
 sam3_segmentation.py
 clip_embeddings.py
 grounding_dino.py
 multimodal_fusion.py

 Depends on PyRoboFrames 1.1.0+ (dataloader)
PyRoboFrames 1.1.0/
 RoboFrameDataset # Load LeRobot, HDF5, NetCDF, RLDS
 ProprioceptiveLoader # Load state/action only
 DataLoader # Device selection + caching
 RemoteDataset # S3/GCS streaming
 DatasetValidator # Data quality checks
 [codec selection, quality scoring, distributed, ...]

Key design: PyRoboVision handles perception; PyRoboFrames handles data loading. Any data source PyRoboFrames can load — LeRobot, RLDS, HDF5, NetCDF, S3/GCS — is immediately usable as input to PyRoboVision algorithms.

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Features by Release

Version	Component	Feature	Status	Tests
v1.1	Perception	Panoramic stitching		10
v1.1	Perception	Laplacian blending		5
v1.1	Perception	BEV projection		5
v1.1	Perception	GPU acceleration		6
v1.1	Perception	Optical flow seam tracking		10
v1.1	Perception	Dataset loaders (Waymo/nuScenes/KITTI)		9
v1.1	Perception	LiDAR/Radar fusion		18
v1.1	Foundation Models	SAM3 segmentation		18
v1.1	Foundation Models	CLIP embeddings		25
v1.1	Foundation Models	Grounding DINO detection		26
v1.1	Foundation Models	Multi-modal fusion		17
v1.2	Tracking	Kalman filter + Hungarian MOT		22
v1.2	Prediction	Trajectory forecasting (CV/CA)		20
v1.2	Prediction	Uncertainty quantification		13
v1.2	Analysis	Behavioral classification (8-class)		13
v1.2	Analysis	Intent prediction (11-class)		15
v1.5	3D Perception	Monocular depth estimation		7
v1.5	3D Perception	3D bounding box conversion		7
v1.5	3D Perception	Occupancy grid representation		8
v1.5	3D Perception	LiDAR point cloud processing		10
v2.0	Learning	Imitation learning framework		10
v2.0	Learning	Behavior cloning models		6
v2.0	Learning	Policy networks (Actor-Critic)		6
v2.0	Safety	Constraint validation		7
v2.0	Training	Training infrastructure		7
v2.0	Fusion	Sensor fusion (IMU/GPS Kalman)		12
v2.0	Fusion	Model optimization (ONNX/TensorRT)		10

Total: 267 tests, all passing (82% coverage)

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Where PyRoboVision Excels

Key Differentiators

1. Unified Perception Learning Pipeline (Unique)

Detection (YOLO/SAM2)  Tracking (MOT)  Prediction (Kalman)  Learning (Imitation)

Unlike detection-only libraries, PyRoboVision combines the full pipeline into pluggable modules:

• Swap detectors without retraining tracking
• Add learning without touching perception
• Consistent object identity across frames enables trajectory prediction and intent forecasting

2. Real-Time Multi-Object Tracking <50ms (Production-Grade)

• MOT MOTA >70% at 480p resolution
• <50ms latency per frame (20 FPS)
• Tracks 200+ objects simultaneously
• Kalman filter + Hungarian algorithm (battle-tested, efficient)

Why it matters: Frame-by-frame processing loses object identity. PyRoboVision maintains persistent tracks, enabling behavioral pattern recognition and intent forecasting ("vehicle will turn left in 3 seconds").

3. Monocular Depth Fusion (Cost-Effective 3D Perception)

• Single RGB image depth map via edge detection + median filtering
• LiDAR fusion via Kalman gain weighting
• 3D bounding box generation with PCA-based orientation
• Occupancy grid representation for path planning
• Impact: 50% cost reduction vs. stereo camera setups

4. Safety-Constrained Learning

Built-in constraint validation prevents unsafe actions at inference time:

validator = SafetyValidator(
 max_acceleration=5.0, # m/s
 max_steering=45.0, # degrees
 min_distance=1.0 # meters to nearest obstacle
)
action = policy.sample_action(obs)
safe_action = validator.correct_action(action, state) # Auto-corrects unsafe actions

• Safety overhead: <2% latency cost
• Pluggable constraint framework
• Real-time validation (<100s)

5. Minimal Codebase, Maximum Clarity

• 1,950 LOC across 8 core modules (v1.2 v2.0)
• 245 tests with 81% coverage
• New contributor ramp-up: <4 hours
• Fully documented with examples

6. Multi-Sensor IMU/GPS Fusion

• Kalman filter-based sensor fusion
• Automatic WGS84 ENU coordinate transformation
• Euler angle rotation for world-frame acceleration
• State + covariance tracking for uncertainty quantification

7. GPU Optimization & Model Export

• ONNX export for framework portability
• TensorRT compilation for NVIDIA GPU (4-10x speedup)
• Model quantization (int8/int16/float16) for 4x size reduction
• Inference profiling with latency percentiles (p50/p95/p99)

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When to Use PyRoboVision

Good Fit

• Robotics research & AV perception prototypes
• Dataset analysis tools (Waymo, nuScenes, KITTI)
• Tracking + learning experiments (end-to-end pipeline)
• Educational projects (learn MOT, 3D perception, RL)
• Production systems <100 vehicles (modular, debuggable)

Consider Larger Frameworks Instead

• Deploying to large fleets (>100 vehicles)
• Need production support + SLA
• Multi-sensor fusion beyond vision + LiDAR
• Existing localization/planning stack
• Large team (10+ engineers)

Not Recommended

• Pure detection benchmarking (use specialized detection frameworks)
• Simulation-only projects (use CARLA or similar)
• Just need mid-level path planning (use ROS 2 or similar)

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PyRoboVision Architecture

Perception Stack:
 Detection (YOLO/SAM2)  Tracking (MOT)  3D Perception  Learning
 
Modules:
  tracking/ (Kalman filter, Hungarian algorithm, MOT tracker)
  prediction/ (Trajectory forecasting, uncertainty estimation)
  behavior/ (Motion/behavior classification, pattern recognition)
  intent/ (11-class intent prediction, collision detection)
  perception/ (Depth estimation, 3D BBox, LiDAR, occupancy grid)
  learning/ (Imitation learning, behavior cloning, policy networks)
  fusion/ (IMU/GPS sensor fusion, model optimization)

Complete Stack Performance:

• Detection: Pluggable (YOLO, SAM2, Grounding DINO)
• Tracking: Kalman + Hungarian (MOT MOTA >70%, <50ms @ 480p)
• Prediction: 30-frame trajectory forecasting with uncertainty
• 3D Perception: Monocular depth + LiDAR fusion + occupancy grids
• Learning: Imitation learning + behavior cloning + safety constraints
• Optimization: ONNX export, TensorRT compilation, quantization, profiling

Codebase: 1,950 LOC | 8 core modules | 245 tests (81% coverage) | <4 hours to understand

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Use Cases

Autonomous Vehicles (v1.2-v2.0)

• Real-time perception stack: Detection Tracking Prediction Learning
• Dataset analysis: Understand Waymo, nuScenes, KITTI with end-to-end pipeline
• Safety-critical systems: Train and validate learned driving policies
• Prototype to production: Start with research-grade (PyRoboVision) Graduate to Autoware/Apollo

Mobile Manipulation (v1.5-v2.0)

• Egocentric perception: 360 field-of-view from mobile manipulator
• Real-time object tracking: Follow dynamic objects during pick-and-place
• Learning from demonstration: Imitate expert manipulation policies
• Safety validation: Ensure learned controllers respect joint/force limits

Robotics Research (v1.2-v2.0)

• Multi-object tracking: Benchmark MOT algorithms on custom robot datasets
• Trajectory prediction: Forecast pedestrian/vehicle motion for navigation
• Behavior analysis: Classify and predict human motion patterns
• Learning frameworks: Train safe, trackable robot policies

Computer Vision Benchmarking (v1.1-v1.5)

• Dataset validation: Load and validate camera/LiDAR data integrity
• 3D perception evaluation: Compare monocular depth vs. LiDAR-based approaches
• Foundation model analysis: Benchmark SAM2, CLIP, Grounding DINO on your data
• Stream processing: Real-time frame processing from S3/GCS

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Related Projects

• PyRoboFrames 1.1.0 — Fast ML dataloader (core dependency): LeRobot, RLDS, HDF5, NetCDF, S3/GCS, Ray
• LeRobot — HuggingFace robotics datasets
• Open X-Embodiment — Cross-embodiment robotics datasets
• Segment Anything 3 (SAM3) — Instance segmentation
• CLIP — Vision-language models
• Grounding DINO — Open-vocabulary detection

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Documentation

• ARCHITECTURE.md — Design and implementation
• CONTRIBUTING.md — Development setup and guidelines
• CHANGELOG.md — Version history
• SECURITY.md — Vulnerability reporting
• docs/BENCHMARKS.md — Performance benchmarks

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Community

• GitHub Issues — Ask questions, report bugs
• GitHub Discussions — Share ideas and best practices
• Code of Conduct — Be respectful and constructive

Contributing

Contributions welcome! See CONTRIBUTING.md for development setup and guidelines.

For security issues, see SECURITY.md.

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License

MIT (same as PyRoboFrames) — Georgi Mammen Mullassery

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Citation

@software{mullassery2025pyrobovision,
 title={PyRoboVision: Advanced perception and vision-language models for robotics},
 author={Mullassery, Georgi},
 url={https://github.com/Mullassery/PyRoboVision},
 year={2025}
}