WOD
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Semantic Segmentation | WOD (val) | mIoU66.63 | 14 | |
| Point Cloud Registration | WOD (test) | mRR89.5 | 12 | |
| Autonomous Driving Planning | WOD-E2E | RFS Overall8.079 | 11 | |
| Trajectory Planning | WOD-E2E | RFS Overall8.079 | 11 | |
| 3D Object Detection | WOD (val) | 3D AP (Vehicle, IoU=0.7)67.25 | 7 | |
| Geometry Generation | WOD Vehicle (val) | Consistency15.87 | 6 | |
| Image Generation | WOD-Vehicle (val) | FID59.5 | 6 | |
| Driving Planning Evaluation | WOD-E2E (val) | RFS7.633 | 4 | |
| Behavior-Trajectory Alignment | WOD-E2E (val) | Longitudinal Error44.2 | 4 | |
| Object-Explanation Consistency | WOD-E2E (val) | O -> E Consistency98.1 | 4 | |
| 3D Object Detection | WOD 100% training data (test) | Vehicle L1 mAP81.7 | 4 | |
| Vision Question Answering | WOD-E2E (val) | BLEU-431 | 3 |
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