Svashasan OS v0.1 Prediction Model Report

Executive Assessment

The svashasan_prediction_phase2 codebase (~31 artifacts) represents a significant engineering leap from the Phase 1 notebook prototype. The architecture is now modular, temporally aware, and multi-modal — a strong foundation for the full Svashasan autonomous stack.

Area	Score (/10)	Status	Verdict
Repository Architecture	8	Good	Proper modularization
Data Pipeline	6	Partial	Strong structure, synthetic gaps remain
Model Architecture	8	Good	Multimodal + temporal reasoning
Training Stack	7	Good	Callbacks + weighted loss
Inference Design	8	Good	Sequence buffering added
Testing	5	Partial	Minimal coverage
Production Readiness	5	Partial	Missing deployment layer
Autonomous Readiness	6	Moderate	Perception progressing

Overall Phase 2 Score: 6.6 / 10

Phase-2 MVP | Strong Foundation

1. Repository Architecture Review

Phase 2 eliminated the single-notebook dependency entirely. The codebase is now a proper Python package with clean separation of concerns across data, models, training, inference, and config layers.

Component	Status	Notes
Data separation		Clean
Model isolation		Good abstraction
Training package		Proper train/eval split
Inference module		Production direction
Config system		YAML present
Deployment module		Empty — needs export_onnx.py, ros2_node.py
Simulation		Empty — needs carla_runner.py, metrics.py
CI/CD		Missing entirely

Architecture Win: Single-notebook dependency fully removed — codebase is now importable as a robust Python package.

2. Data Layer Review (/data)

Modality Strengths

Sequence loading (T frames)
Temporal batching
Multi-sensor support
Camera preprocessing
IMU preprocessing (6-axis)
GPS preprocessing (relative)
LiDAR BEV embedding

Structural Gaps

Issue	Priority
LiDAR embedding-only layout	High
Missing timestamp sync layer	Critical
Sensor validation missing on load	Medium
Dataset schema checks absent	Medium

Integrity Directives Needed: timestamp_align() | validate_modalities() | sync_sensors()

3. Model Architecture Review (/models)

CNN Branch (/cnn.py)

Inspired by the DAVE-2 blueprint incorporating BatchNorm, Dropout layers, and configurable filter dimensions. Integrated with TimeDistributed wrapper frameworks to facilitate shared weight evaluation sequentially across all T active frames.

Temporal Layer (/temporal.py)

LSTM encoder (2-layer stacked)	Active
Transformer encoder	Phase 3 Stub
Sequence processing support
Drop-in module wrapper designs	Config-driven only

Fusion Model (/fusion.py)

The 6-branch temporal fusion engine represents the primary algorithmic progress of Phase 2. Inputs are mapped per-frame via unified TimeDistributed layers, concatenated sequentially, and routed through a stacked 2-layer LSTM stack prior to processing in dual-output command heads.

Multi-modal fusion (6 inputs)

Sequence reasoning flow Stacked LSTM

Dual command heads (steer/throttle)

YOLO perception alignment

Attention-driven fusion module Phase 3

Trajectory projection pathways Planned Add

Occupancy forecast pipeline Critical Gap

Autonomous planning head Critical Gap

4. Perception Review (/perception.py)

YOLOv8n architecture is aligned to AV navigation requirements via specialized category constraints, scene coordinate generation, and strongly typed output layouts.

Currently Implemented

YOLO AV category filtering
Scene coordinate mappings
DetectionResult structures

Planned Extensions

Dynamic object tracking (DeepSORT)
Lane boundary estimation (LaneNet)
Depth computation networks (MiDaS)

5. Training Review (/trainer.py)

Feature	Status
Weighted steering/throttle loss (0.7 / 0.3)
Early stopping mechanisms
Best model checkpoint exports
TensorBoard logging alignment
MLflow platform synchronization
Mixed precision execution (Float16)	Verify setting flag

6. Evaluation Review (/evaluate.py)

Configured Local Diagnostics

Steering error indicators

Throttle error metrics

In-sequence variation rates

Worst-case tail limits (P95)

Required Target Autonomy Metrics

Steering command jerkHigh priority

Geometric deviation vectorsHigh priority

Driver takeover occurrencesCritical Gap

Disengagement frequencyCritical Gap

7. Inference Review (/predictor.py)

Primary production asset: Code structure meets modular implementation guidelines.

Rolling sensor input buffer

Deque mapping active

Per-frame processing loops

Target latency metrics defined

Typed controller return structures

Fully integrated

Pre-processing pipelines

Fully integrated

Asynchronous inputs pipeline

Planned add

ONNX runtime deployment paths

Requires integration

TensorRT optimized execution

Requires integration

ROS2 wrapper nodes

Vehicle execution priority

8. Testing Review

Current coverage limits: ~20–25% of source paths (active package: tests/test_models.py)

Model verification: Partial Loader tests End-to-end pipelines Execution benchmarks

Target Test Suite Directory Structure: test_loader.py · test_preprocess.py · test_inference.py · test_training.py · test_pipeline.py

9. Empty Modules — Critical Gap

Folder	Status & Priority	Files Needed
deployment/	Empty High	`export_onnx.py`, `tensorrt.py`, `ros2_node.py`
simulation/	Empty Critical	`carla_runner.py`, `metrics.py`, `scenario_runner.py`

10. Technical Debt Summary

Empty deployment stack (ONNX, TensorRT, ROS2) High
Empty simulation layer (CARLA integration) Critical
Transformer temporal encoder modules Medium
Visual tracking and object path estimation (DeepSORT) High
Aut autonomy planning pipelines Critical

11. Founder Readiness Assessment

Phase Progress

Phase 1 Complete

Notebook prototype layouts

Phase 2 Current

Modular prediction engine architecture

Phase 3 In Progress

Transformer layers + path projection heads

Estimated AV Stack Maturity

Prediction Engine 70%

Perception 45%

Planning 5%

Overall AV Stack Integration 30%

12. Benchmark Performances & Targets

Performance Artifact Status: The repository currently defines how performance will be measured, but no active benchmark run artifacts exist (metrics.json, MLflow runs, checkpoints, evaluation outputs, etc.). The codebase does not contain actual training outputs or saved evaluation runs, so achieved benchmark performance is not reported yet. What exists below are benchmark targets and the evaluation framework.

Phase-2 Benchmark Targets

Category	Metric	Target	Unit	Status
Steering	MAE	< 0.04	rad	Target defined
Steering	MAE	< 2.3	degrees	Equivalent target
Steering	RMSE	Not specified	rad	Post-Training
Steering	R²	Close to 1.0	score	Post-Training
Throttle	MAE	< 0.05	normalized	Target defined
Temporal	Steering Consistency	< 0.02	rad/frame	Target defined

Evaluation Suite Mathematical Formulations

Verification math deployed in the training/evaluate.py evaluation script:

Metric Group	Implemented	Formula
MAE (Steer/Throttle)		mean(abs(y_true − y_pred))
RMSE (Steer/Throttle)		sqrt(mean((y_true − y_pred)²))
Steering P95 Error		percentile(abs(error), 95)
Temporal Steering Δ		mean(diff(predictions))

Production Acceptance Boundaries

Metric	Acceptable	Production Target
Steering MAE	0.04 rad	≤0.02 rad
Steering RMSE	0.06	≤0.03
Steering R²	0.80	≥0.95
Throttle MAE	0.05	≤0.02
Inference Latency	<100 ms	<20 ms

Local Diagnostics Readiness

Benchmark Framework 100%

Metrics Implementation 90%

Trained Model Run Outputs 0%

Run Evaluation Command Pipeline

evaluator = Evaluator(config)

report = evaluator.evaluate(
    model=model,
    test_ds=test_dataset
)

evaluator.print_report(report)

System Evaluation Verdict

This is no longer a prototype notebook. Svashasan now has an early autonomy platform foundation — modular, temporally aware, and multi-modal. The biggest blockers are the planning stack, simulation layer, and deployment pipeline.

Primary Wins

Modular Python package architecture
LSTM temporal reasoning (2-layer stacked)
6-input multi-sensor fusion model
Dual-control outputs (steering + throttle)
Real-time rolling-buffer inference

Core Architectural Obstacles

Critical No planning stack
Critical No simulation environment
High Empty deployment layer
High No trajectory prediction head