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# TriDrive checkpoints (supplementary)

Only TriDrive's own models and our reproduction of the published Driver-WM pipeline are included. Third-party weights
(V-JEPA 2, YOLO11x-pose, RTMW-x-l, MotionBERT release, OpenBADAS) are not redistributed; other baselines (ST-GCN, SiMLPe,
MotionBERT fine-tunes, Driver-WM architecture adapted to our tokens) and ablation arms (independent branches, no links,
all-to-all, visual-only, sensor-only, road-to-driver) are available on request. Internal run names keep the development
code name `cwm3d` / `CockpitWM`; they denote the same model as `TriDrive` in the paper.

Each run directory contains the checkpoint (`ckpt_best.pt` or `ckpt_ep*.pt`), the run manifest (`run_*_latest.json` / `config.json`: full command line and hyper-parameters), training history and, where the run was evaluated, the test/validation metrics that the paper quotes (the compact forecasters `KPF_*` and the pose-pretraining checkpoint ship history and manifests only). Absolute paths in manifests and inside the pickled `args` / `cfg` dictionaries were rewritten to `/home/USER/...` (see `EXPORT_NOTE*.json`); the host name was replaced by `REDACTED`. Licence: CC BY 4.0 (`LICENSE`); checksums in `MANIFEST.txt`.

## `aide/` — AIDE keypoint forecasting (Table 1, Table 2, Appendix C)

| Directory | Paper row | Seeds |
|---|---|---|
| `cwm3d_kp_s{42,123,456,789,1011}_v2` | Ours (5.77M), no pretraining: 48.60 ± .12 All-MPJPE | 5 |
| `cwm3d_kp_batonpre_trunkp123_s{42,123,456,789,1011}_v2` | Ours + BATON pose pretraining: 48.05 ± .06 | 5 |
| `cwm3d_kp_batonpre_pretrain_s123` | The BATON pose-pretraining checkpoint whose trunk is transferred (epoch 4 of 6) | 1 |
| `cwm3d_kp_abs_s{42,123,456}_v2` | Absolute-coordinate ablation of the same model (3.87 px worse) | 3 |
| `cwm3d_vis_sema_vge_s{42,123,456}_v2` | View-gate semantic variant (73.5M): `ckpt_best_f1.pt` = macro-F1-selected row of Table 2; `ckpt_best.pt` = geometry-selected row of Appendix C | 3 |
| `driverwm_repro_s{42,123,456}_v2` | Our reproduction of the published Driver-WM pipeline (123.7M): 130.86 ± 3.11 All-MPJPE, 194.34 HM h=5 | 3 |

## `baton/` — BATON joint forecasting (Table 3, Appendix B)

| Directory | Role |
|---|---|
| `I1_all_minus_road2drv_s{0,1,2}_final_ecan` | Directed joint model on ECAN inputs without forecast features: the "Directed (default)" / "ECAN, directed (default)" columns of Table 3 A–C |
| `I1_all_minus_road2drv_s{0,1,2}_final_ecan_kpf` | Default registered joint model with the frozen forecast features injected: the "Default" column of Table 3 D |
| `U0_kin_can_noK_s{0,1,2}_kcv2_clip8_ln` | Pretrained driver branch (GRU over 18-d kinematics + CAN) |
| `V0_can4s_s{0,1,2}_stats2` | Pretrained vehicle branch (causal TCN over 4 s of CAN) |
| `E0_pooled_P_can_s{0,1,2}_v2` | Pretrained road branch on ECAN inputs (causal Transformer over 16 V-JEPA 2 tokens + side MLP) |
| `KPF_mb_full_s{0,1,2}` | Compact keypoint forecaster (0.85M) that produces the 10-d forecast features $f_t$; `s0` is the deployed one. Architecture: the MotionBERT DSTformer re-instantiated and trained from scratch (no released weights; Apache-2.0 attribution in `../THIRD_PARTY_NOTICES.md`) |
| `KPF_cwm_full_s{0,1,2}` | Alternative compact forecaster architecture evaluated in Appendix B |
| `warning_head/` | Training logs, cross-validation tables and selected configurations of the current-state warning probe (histogram gradient boosting over 77 features), including the sub-runs fitted on ablation trunks; the fitted probe itself is `deployed/readout/readout_cur_dwfc_s0.pkl` |
| `warning_trunk_heads/` | Latent-state warning probe experiment of Appendix D |

## `deployed/` — the on-vehicle configuration (Section 3.4, Appendix G)

| File | Role |
|---|---|
| `onnx/joint_final_ecan_kpf_s0.onnx` | Joint model as executed on the chestnut eGPU (2.35 Hz) |
| `onnx/kpf_mb_full_s0.onnx` | Compact keypoint forecaster as executed on the vehicle |
| `onnx/student_v5/` | Distilled ViT-S/16 road student (v5, deployed): pooled road feature + risk logit. Derivative of V-JEPA 2 and OpenBADAS; check both licences before redistribution |
| `aux/` | Feature standardization statistics and the PCA projections (256-d road tokens, 256-d visual side vector) fitted on the training split |
| `readout/` | The deployed HGB probe (`readout_cur_dwfc_s0.pkl`), its 77 feature names, fill constants, and the validation-fitted thresholds |

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	# TriDrive checkpoints (supplementary)

	Only TriDrive's own models and our reproduction of the published Driver-WM pipeline are included. Third-party weights
	(V-JEPA 2, YOLO11x-pose, RTMW-x-l, MotionBERT release, OpenBADAS) are not redistributed; other baselines (ST-GCN, SiMLPe,
	MotionBERT fine-tunes, Driver-WM architecture adapted to our tokens) and ablation arms (independent branches, no links,
	all-to-all, visual-only, sensor-only, road-to-driver) are available on request. Internal run names keep the development
	code name `cwm3d` / `CockpitWM`; they denote the same model as `TriDrive` in the paper.

	Each run directory contains the checkpoint (`ckpt_best.pt` or `ckpt_ep.pt`), the run manifest (`run__latest.json` / `config.json`: full command line and hyper-parameters), training history and, where the run was evaluated, the test/validation metrics that the paper quotes (the compact forecasters `KPF_` and the pose-pretraining checkpoint ship history and manifests only). Absolute paths in manifests and inside the pickled `args` / `cfg` dictionaries were rewritten to `/home/USER/...` (see `EXPORT_NOTE.json`); the host name was replaced by `REDACTED`. Licence: CC BY 4.0 (`LICENSE`); checksums in `MANIFEST.txt`.

	## `aide/` — AIDE keypoint forecasting (Table 1, Table 2, Appendix C)

	\| Directory \| Paper row \| Seeds \|
	\|---\|---\|---\|
	\| `cwm3d_kp_s{42,123,456,789,1011}_v2` \| Ours (5.77M), no pretraining: 48.60 ± .12 All-MPJPE \| 5 \|
	\| `cwm3d_kp_batonpre_trunkp123_s{42,123,456,789,1011}_v2` \| Ours + BATON pose pretraining: 48.05 ± .06 \| 5 \|
	\| `cwm3d_kp_batonpre_pretrain_s123` \| The BATON pose-pretraining checkpoint whose trunk is transferred (epoch 4 of 6) \| 1 \|
	\| `cwm3d_kp_abs_s{42,123,456}_v2` \| Absolute-coordinate ablation of the same model (3.87 px worse) \| 3 \|
	\| `cwm3d_vis_sema_vge_s{42,123,456}_v2` \| View-gate semantic variant (73.5M): `ckpt_best_f1.pt` = macro-F1-selected row of Table 2; `ckpt_best.pt` = geometry-selected row of Appendix C \| 3 \|
	\| `driverwm_repro_s{42,123,456}_v2` \| Our reproduction of the published Driver-WM pipeline (123.7M): 130.86 ± 3.11 All-MPJPE, 194.34 HM h=5 \| 3 \|

	## `baton/` — BATON joint forecasting (Table 3, Appendix B)

	\| Directory \| Role \|
	\|---\|---\|
	\| `I1_all_minus_road2drv_s{0,1,2}_final_ecan` \| Directed joint model on ECAN inputs without forecast features: the "Directed (default)" / "ECAN, directed (default)" columns of Table 3 A–C \|
	\| `I1_all_minus_road2drv_s{0,1,2}_final_ecan_kpf` \| Default registered joint model with the frozen forecast features injected: the "Default" column of Table 3 D \|
	\| `U0_kin_can_noK_s{0,1,2}_kcv2_clip8_ln` \| Pretrained driver branch (GRU over 18-d kinematics + CAN) \|
	\| `V0_can4s_s{0,1,2}_stats2` \| Pretrained vehicle branch (causal TCN over 4 s of CAN) \|
	\| `E0_pooled_P_can_s{0,1,2}_v2` \| Pretrained road branch on ECAN inputs (causal Transformer over 16 V-JEPA 2 tokens + side MLP) \|
	\| `KPF_mb_full_s{0,1,2}` \| Compact keypoint forecaster (0.85M) that produces the 10-d forecast features $f_t$; `s0` is the deployed one. Architecture: the MotionBERT DSTformer re-instantiated and trained from scratch (no released weights; Apache-2.0 attribution in `../THIRD_PARTY_NOTICES.md`) \|
	\| `KPF_cwm_full_s{0,1,2}` \| Alternative compact forecaster architecture evaluated in Appendix B \|
	\| `warning_head/` \| Training logs, cross-validation tables and selected configurations of the current-state warning probe (histogram gradient boosting over 77 features), including the sub-runs fitted on ablation trunks; the fitted probe itself is `deployed/readout/readout_cur_dwfc_s0.pkl` \|
	\| `warning_trunk_heads/` \| Latent-state warning probe experiment of Appendix D \|

	## `deployed/` — the on-vehicle configuration (Section 3.4, Appendix G)

	\| File \| Role \|
	\|---\|---\|
	\| `onnx/joint_final_ecan_kpf_s0.onnx` \| Joint model as executed on the chestnut eGPU (2.35 Hz) \|
	\| `onnx/kpf_mb_full_s0.onnx` \| Compact keypoint forecaster as executed on the vehicle \|
	\| `onnx/student_v5/` \| Distilled ViT-S/16 road student (v5, deployed): pooled road feature + risk logit. Derivative of V-JEPA 2 and OpenBADAS; check both licences before redistribution \|
	\| `aux/` \| Feature standardization statistics and the PCA projections (256-d road tokens, 256-d visual side vector) fitted on the training split \|
	\| `readout/` \| The deployed HGB probe (`readout_cur_dwfc_s0.pkl`), its 77 feature names, fill constants, and the validation-fitted thresholds \|