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FrontDrive — single-front-camera NAVSIM planners (Drive-JEPA perception-based, continued on all of navtrain)

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`

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).

Where the FrontDrive models do better than the released checkpoint

released checkpoint rolls into the stopped car ahead	released checkpoint rolls into the crossing car

released checkpoint leaves the drivable area (curb)	released checkpoint veers off the road

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).

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

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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