Buckets:
| # TriDrive — code, checkpoints, benchmark tables, derived data, and user-study results | |
| TriDrive is a unified framework that jointly models the driver, the vehicle, and the road for two applications: | |
| multi-step forecasting of driver, vehicle, and road-demand states, and a real-time current-state warning probe for driver | |
| monitoring. Three modality-specific predictors (anchored driver kinematics from a frozen pose estimator, CAN-bus vehicle | |
| dynamics, and frozen V-JEPA 2 road latents with structured road margins) are connected by directed residual connections into | |
| the vehicle state and decode 25 future steps (0.2 s) of transition hazards, continuous vehicle dynamics, and future road | |
| latents directly from the encoded history. The framework is evaluated on the public AIDE benchmark, on the BATON | |
| naturalistic driving corpus, and on a comma four with a chestnut eGPU in a paired on-road user study with 14 drivers. | |
| This bucket accompanies an anonymous submission under double-blind review. Internal identifiers keep the development code | |
| name `CockpitWM` / `cwm3d`; they denote the same model as `TriDrive` in the paper. | |
| ## Contents | |
| | Path | What it is | Licence | | |
| |---|---|---| | |
| | `code/` | Training / evaluation code snapshot (AIDE forecaster, joint model, warning probe, baselines, table generation). Layout in `code/README.md` | MIT (`LICENSE`) | | |
| | `checkpoints/` | TriDrive checkpoints and our Driver-WM reproduction: `aide/` (keypoint forecaster, 5+5 seeds, pretraining trunk, ablations, semantic variant, Driver-WM reproduction), `baton/` (joint model, branch predictors, compact keypoint forecaster, warning probe), `deployed/` (on-vehicle ONNX graphs, PCA/stats, probe). Mapping to paper rows in `checkpoints/README.md`; checksums in `checkpoints/MANIFEST.txt` | CC BY 4.0 (`checkpoints/LICENSE`) | | |
| | `benchmark/labels/` | Warning-probe label tables: 979 training anchors (562 manual / 417 engaged), the 512-row gold set and the 509-row headline gold set | CC BY-NC 4.0 | | |
| | `benchmark/human_eval_299/` | The 299-clip human evaluation: clip manifest, three-stage labels, frozen thresholds, pre-registration (byte-identical, hash included) and result tables | CC BY-NC 4.0 | | |
| | `benchmark/aide_protocol/` | AIDE split files, protocol and metric code, protocol verification record | MIT / AIDE terms for the data itself | | |
| | `benchmark/tables/` | Every result table behind the paper: release tables (AIDE, BATON, driver-monitoring baselines), the generated LaTeX tables, run ledgers, Driver-WM audit and quantile sweeps, EMA confound check | CC BY 4.0 | | |
| | `data/` | Pseudonymised derived data used by the joint model: anchor grids with CAN histories and targets, road-token bank and anchor index, road PCA packs, ECAN margins and road-dynamics targets, demand / pressure / response targets, PCA matrices. Details in `data/README.md` | CC BY-NC 4.0 (`data/LICENSE-DATA`, inherited from BATON) | | |
| | `questionnaire/` | The fielded questionnaire (blank), the 14 completed forms anonymised as `driver01`–`driver14` (= `P01`–`P14` in the paper), per-item answers, per-participant construct scores, endpoint statistics, analysis scripts and the figure sources | CC BY 4.0 (`questionnaire/LICENSE`) | | |
| | `THIRD_PARTY_NOTICES.md` | Licences of the components TriDrive builds on | — | | |
| ## Identifier scheme | |
| BATON devices and routes appear only as pseudonyms: `driver_N` for a device, `driver_N__route_M__route_a-b` for a route | |
| segment group (`route_a-b` = the segment range of the original recording); a recording id that could not be resolved to a route group appears as a date-free `rid_<10 hex>`. The pseudonyms are consistent across every file | |
| in this bucket and with the public BATON release. Raw device identifiers, recording timestamps, file-system paths, hosts and | |
| annotator identities were removed; where a value had to be kept for joins it is the pseudonym. | |
| ## What is not included | |
| * **Driver-side cabin-derived features** (kinematics, face/head-pose channels, keypoint residual banks). They are derivatives | |
| of in-cabin video of BATON drivers and are withheld pending consent review; the code in `code/data/` rebuilds them from | |
| the BATON corpus. | |
| * **Raw recordings.** No BATON or AIDE video, CAN logs, pose data or frames; no on-road recordings of the user study. | |
| * **Hold-out device packs** (60 additional BATON devices) and **per-sample prediction dumps** of the joint model (≈ 21 GB); | |
| the metrics computed from them are in `benchmark/tables/`. Available on request after the review period. | |
| * **AIDE-derived caches and per-clip predictions** (AIDE is obtained from its authors; `benchmark/aide_protocol/` rebuilds them). | |
| * **Third-party weights** (V-JEPA 2, OpenBADAS, YOLO11x-pose, RTMW, MotionBERT release weights); see `THIRD_PARTY_NOTICES.md`. | |
| * Baseline and ablation checkpoints other than the Driver-WM reproduction (ST-GCN, SiMLPe, MotionBERT fine-tunes, | |
| independent / no-link / all-to-all / visual-only / sensor-only arms): available on request. | |
| ## Headline numbers (from the paper; every table is under `benchmark/tables/`) | |
| | Result | Where | | |
| |---|---| | |
| | AIDE keypoint forecasting: 48.60 ± 0.12 All-MPJPE without pretraining, 48.05 ± 0.06 with BATON pose pretraining (5 seeds each) | Table 1, Appendix C; `checkpoints/aide/` | | |
| | BATON joint forecasting: directed residual connections and road margins vs. independent branches, with paired driver-cluster bootstrap intervals | Table 3, Appendix B; `checkpoints/baton/`, `benchmark/tables/` | | |
| | Warning probe: AUROC 0.725 (manual) vs. 0.563 for the openpilot-based baseline on the human-labelled gold set | Section 4.4, Appendix D; `benchmark/labels/` | | |
| | Human evaluation of 299 clips, pre-registered | Appendix E; `benchmark/human_eval_299/` | | |
| | On-vehicle: pose 10 Hz, keypoint forecaster and joint model 2.35–5 Hz, end-to-end p95 177 ms on a comma four + RX 9060 eGPU | Section 3.4, Appendix G; `checkpoints/deployed/` | | |
| | Paired on-road user study, 14 drivers: appropriateness +1.79 [0.86, 2.75] (n = 14), timeliness +2.67 [1.33, 4.08] (n = 12), annoyance +1.17 [0.33, 2.08] (n = 12); preference 7 : 4 : 3 (TriDrive : stock : no difference) | Section 4.4, Appendix H; `questionnaire/` | | |
| ## Reproducing the tables | |
| * **AIDE (Table 1, Appendix C):** `code/aide/train/train_kp.py` trains one arm per seed; `code/aide/eval/` computes All-MPJPE, | |
| PCK@0.05 and the high-motion quantile sweeps. Checkpoints under `checkpoints/aide/` are the exact runs quoted in the paper. | |
| * **BATON (Table 3, Appendix B):** `code/train/` trains the branch predictors and the joint model on the packs in `data/` | |
| (driver-side features must be rebuilt first, see above); `code/eval/` produces the onset PR-AUC tables with paired | |
| driver-cluster bootstrap intervals from `checkpoints/baton/`. | |
| * **Warning probe (Section 4.4, Appendix D):** `code/eval/` and `code/baselines_dms/` evaluate the probe | |
| (`checkpoints/baton/warning_head/`, `checkpoints/deployed/readout/`) on `benchmark/labels/gold_509.csv`. | |
| * **Human evaluation (Appendix E):** `benchmark/human_eval_299/` with `code/eval/newtest_analysis.py`. | |
| * **User study (Appendix H):** `questionnaire/scripts/analysis_questionnaire_v1.py` on `questionnaire/results/questionnaire_wide.csv`. | |
| ## Environment | |
| Python 3.12, PyTorch, ONNX Runtime, scikit-learn, `huggingface_hub` for the frozen encoders. Paths in the scripts use the placeholder prefix `/home/USER/...` (the code resolves its own tree relative to `__file__`; `AIDE_ROOT` is read from the environment); replace the placeholders that point at data locations before running. Hosts, IP addresses and the | |
| remote data server referenced by the original training-queue scripts were replaced by placeholders. | |
| ## Licence | |
| Code: MIT (`LICENSE`). Model weights and questionnaire materials: CC BY 4.0. Derived data and labels: CC BY-NC 4.0, inherited | |
| from the BATON corpus. Third-party components: see `THIRD_PARTY_NOTICES.md`. Attribution: "TriDrive authors (anonymous for | |
| double-blind review)"; the camera-ready version will add the citation. | |
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