| --- |
| license: cc-by-4.0 |
| tags: |
| - autonomous-driving |
| - navsim |
| - end-to-end-planning |
| - drive-jepa |
| - single-camera |
| --- |
| |
| # FrontDrive — single-front-camera NAVSIM planners (Drive-JEPA perception-based, continued on all of navtrain) |
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| Three checkpoints of the same recipe (three seeds), submitted together to the NAVSIM v1 navtest leaderboard as a multi-seed entry. |
| Each is the released Drive-JEPA perception-based ViT-L checkpoint continued for 5 epochs on **all 1,192 navtrain logs** (train + val, |
| 103,288 samples) with Adam 3e-5 (backbone 0.1×), cosine decay to 3e-6 after 200 warm-up steps, effective batch 64, and the five |
| per-epoch fp16 exports averaged into one weight soup. Input: the front camera only (CAM_F0 at t and t−0.5 s, 512×256) plus the ego |
| status; the side cameras are used only through their calibration (no side-camera pixels). Output: one trajectory per scene. |
| |
| | file | seed | navtest PDMS | sha256 (prefix) | |
| |---|---|---|---| |
| | `pb_off_ft_all_s0_soup_1_5.ckpt` | 0 | 93.86 | `ac0331f9a5d44ec1` | |
| | `pb_off_ft_all_s1_soup_1_5.ckpt` | 1 | 93.78 | `c7cb6ca5617cdb8f` | |
| | `pb_off_ft_all_s2_soup_1_5.ckpt` | 2 | 93.94 | `fc0ca06034e85939` | |
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| Mean 93.86 (SD 0.08). The released Drive-JEPA checkpoint scores 93.69 with the same evaluation code; our from-scratch baseline |
| (e50 encoder, upstream recipe, dev-selected) 93.50. Sub-scores relative to the released checkpoint: more ego progress (+1.0), |
| higher drivable-area compliance (+0.35), slightly lower no-at-fault-collision (−0.4), TTC (−0.8…−1.2) and driving-direction (−1.0). |
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| ## Where the FrontDrive models do better than the released checkpoint |
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| | released checkpoint rolls into the stopped car ahead | released checkpoint rolls into the crossing car | |
| |---|---| |
| |  |  | |
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| | released checkpoint leaves the drivable area (curb) | released checkpoint veers off the road | |
| |---|---| |
| |  |  | |
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| Green: FrontDrive seed 0; red: released Drive-JEPA checkpoint; white dashed: human driver. Rendered from the navtest metric cache and |
| the two models' trajectories by `frontdrive/viz/navtest_showcase.py` in the code repository (images are hosted there). |
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| **Loading.** Each file is a Lightning-style state dict (`{"state_dict": {"agent._pad_model.*": …}}`, fp16) that |
| `DriveJEPAAgent.initialize()` loads when passed as `agent.checkpoint_path` (navsim v1 config |
| `agent=drive_jepa_perception_based_agent agent.config.latent=False +agent.config.side_cams_from_calibration=true`). Code, patches |
| and the exact training / evaluation scripts: https://github.com/WangYuHang-cmd/FrontDrive |
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| **Provenance.** Derived from `LinhanWang/Drive-JEPA` `drive_jepa_perception_based_agent_vitl.ckpt` (Drive-JEPA, Apache-2.0 code) |
| and trained only on public NAVSIM navtrain data; navtest was never used for training or model selection. |
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