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Implemented U-NET ARCHITECTURE

UNet

Tokenizer error map comparison

BEV COMPARISON

bev_comparison

TOKENIZER BEV ERROR MAP- RECONSTRUCTION

bev_error_map

DEPTH_SCATTER_COMPARISON

Depth_scatter_comparison

GIF COMPARISON 10 SEC SEQUENCE- GROUND TRUTH - TOKENIZER - WORLD MODEL

sequence_comparison

LIDAR CAMERA OVERLAY

lidar_camera_overlay

KITTI 3D FRONT

KITTI 3D FRONT

ERROR HISTOGRAM

ERROR_HISTOGRAM

Model Checkpoints

Pre-trained model checkpoints are available on Hugging Face.

Tokenizer

The tokenizer checkpoint is saved at step 100,000, which showed the best reconstruction performance.

Download: tokenizer/checkpoint_step_100000.pt

Checkpoint Comparison

Checkpoint MAE (m) RMSE (m) < 1m (%) VQ Perplexity
Step 100,000 1.15 2.37 72.17% 159.23
Step 110,000 3.61 4.66 2.59% 145.19
Step 120,000 3.45 4.35 2.37% 140.94
Step 130,000 3.38 4.31 2.89% 141.39

Selected: Step 100,000 - Best depth reconstruction accuracy with 72.17% of points within 1m error.

World Model

The world model checkpoint is saved at step 50,000 (final checkpoint after full training).

Download: world_model/checkpoint_step_50000.pt

Checkpoint Comparison

Checkpoint Step Val Acc Val Loss
checkpoint_0046000.pt 46,000 83.47% 1.4996
checkpoint_0048000.pt 48,000 80.74% 1.6320
checkpoint_0050000.pt (final) 50,000 84.32% 1.4603
checkpoint_latest.pt 50,000 same same

Selected: Step 50,000 - Final trained model with best validation accuracy of 84.32%.


MNIST DATA VALIDATION

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implementation of Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

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