Release AutoE2E v1.0

Official release with nuPlan Epoch 5 and KITScenes Epoch 5. Adds config.json, architecture and prediction images, and a new model card. Removes the superseded nuPlan Epoch 4 and KITScenes Epoch 7 checkpoints.

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/autoe2e_architecture.jpg filter=lfs diff=lfs merge=lfs -text
+assets/prediction_kitscenes_epoch5_val_roundabout.jpg filter=lfs diff=lfs merge=lfs -text
+assets/prediction_nuplan_epoch5_test_forest_turn.jpg filter=lfs diff=lfs merge=lfs -text
+assets/prediction_nuplan_epoch5_test_night_turn.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,85 +1,234 @@
 ---
 license: other
+license_name: autoe2e-checkpoint-notice
+license_link: https://huggingface.co/AutowareFoundation/auto_e2e#license
 library_name: pytorch
+pipeline_tag: robotics
+datasets:
+- KIT-MRT/KITScenes-Multimodal
 tags:
+- autoware
 - autonomous-driving
-- bevformer
+- end-to-end
+- e2e
 - trajectory-planning
+- motion-planning
+- multi-camera
+- bird-eye-view
+- bevformer
 - nuplan
 - kitscenes
-- experimental
+- robotaxi
 ---
 
-# AutoE2E checkpoints
+<p align="center">
+  <img src="https://huggingface.co/AutowareFoundation/auto_e2e/resolve/main/assets/autoe2e_logo.jpg" alt="AutoE2E" width="100%">
+</p>
+
+<p align="center">
+  <a href="https://github.com/autowarefoundation/auto_e2e"><img src="https://img.shields.io/badge/GitHub-auto__e2e-181717?logo=github" alt="GitHub"></a>
+  <a href="https://d2itskdqq39tx1.cloudfront.net/"><img src="https://img.shields.io/badge/Dashboard-DataModelConsole-0ea5e9" alt="DataModelConsole dashboard"></a>
+  <a href="https://discord.com/invite/Q94UsPvReQ"><img src="https://img.shields.io/discord/953808765935816715?label=Autoware%20Discord&logo=discord" alt="Discord"></a>
+  <a href="https://huggingface.co/AutowareFoundation/auto_e2e"><img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2FAutowareFoundation%2Fauto_e2e&query=%24.downloads&label=downloads%2Fmonth&logo=huggingface&color=FFD21E" alt="Hugging Face downloads"></a>
+</p>
+
+# AutoE2E: End-to-End AI for Self-Driving
+
+AutoE2E is a free and fully open-source End-to-End AI model from the [Autoware Foundation](https://autoware.org/). It enables autonomous driving across highways, arterial roads and city streets using cameras only, without reliance on HD maps. One network turns surround-view camera images and the vehicle's own motion history into the future driving trajectory.
+
+AutoE2E outputs can be fused with physics-based sensors such as LiDAR and radar to power fully driverless robotaxi applications. The baseline camera-only model can enable L2++ automotive ADAS applications for point-to-point hands-free navigation.
+
+This repository hosts the **AutoE2E v1.0 release**: two trajectory-planning checkpoints, the evaluation reports behind the published metrics, and the KITScenes validation shards used by the community benchmark.
+
+| Checkpoint | Trained on | Use it for |
+|---|---|---|
+| [`models/nuplan-epoch-5`](./models/nuplan-epoch-5) | nuPlan v1.1 train sensor logs | The baseline model. Zero-shot evaluation on new cities, and the starting point for fine-tuning on your own data |
+| [`models/kitscenes-epoch-5`](./models/kitscenes-epoch-5) | nuPlan Epoch 5, fine-tuned on KITScenes-Multimodal train | Driving in Karlsruhe and the KITScenes camera rig |
+
+## Vision and goals
+
+Autonomous driving software has been built from many hand-engineered modules and, increasingly, from closed end-to-end models that nobody outside a company can study or improve. AutoE2E takes the opposite path. Everything is developed in the open by the Autoware community: the model code, the data pipelines, the training platform, the evaluation scripts, and the checkpoints published here.
+
+The goals of the project are:
+
+- deliver a camera-first driving model that works on highways, arterial roads and city streets without HD maps;
+- keep the full stack reproducible, so anyone can retrain, benchmark and fine-tune AutoE2E on their own vehicles and cities;
+- provide a planning core that can be combined with LiDAR/radar safety layers for driverless robotaxis, and used camera-only for L2++ ADAS;
+- benchmark openly against other end-to-end models on shared data and shared scripts ([issue #210](https://github.com/autowarefoundation/auto_e2e/issues/210)).
+
+The work happens in the Autoware Robotaxi working group. New contributors are welcome; the [onboarding guide](https://github.com/autowarefoundation/auto_e2e/blob/main/ONBOARDING.md) explains how to join the weekly meetings.
+
+## Architecture
+
+<img src="https://huggingface.co/AutowareFoundation/auto_e2e/resolve/main/assets/autoe2e_architecture.jpg" alt="AutoE2E architecture" width="100%">
+
+AutoE2E is designed as three cooperating models:
+
+- a **Reactive model** at 10 Hz that plans the trajectory in real time from multi-view images, a map raster and ego-motion history;
+- a **World Action Model** at 1 Hz that learns to predict future visual features (JEPA-style) from the encoded visual history;
+- a **Reasoning model** at 1 Hz that detects edge-case scenarios and conditions the planner.
+
+The v1.0 checkpoints contain the **Reactive model**, which is the part that drives. The World Action and Reasoning branches are disabled in these weights and remain under active development.
+
+| Component | v1.0 checkpoint configuration |
+|---|---|
+| Cameras | 6 surround views at 512 × 512; the front camera is also encoded at 1024 × 1024 |
+| Camera history | 8 frames at 0.5 s spacing (3.5 s), BEVFormer V2 temporal fusion |
+| Camera BEV encoder | ResNet-50 + BEVFormer V2 (T8), 300 × 200 BEV grid covering 180 m × 120 m (60 m behind to 120 m ahead), frozen from the official BEVFormer V2 R50 T8 initialization |
+| Map input (optional) | 14-channel semantic map raster plus a 2-channel route raster, 450 × 300 cells at 0.4 m. It can be left empty; the KITScenes Test results below use cameras only |
+| Map/image fusion | Deformable cross-attention from map features into the camera BEV |
+| Ego-motion history | 6.4 s at 10 Hz: speed, longitudinal acceleration, yaw rate, curvature |
+| Trajectory planner | Deterministic GRU planner with deformable BEV feature lookup |
+| Output | 64 steps × (longitudinal acceleration, curvature) at 10 Hz = 6.4 s, integrated into an ego-frame XY trajectory |
+| Auxiliary heads | 8-class BEV segmentation and route reconstruction (training signals) |
+| Parameters | 79.9 M |
+
+## Results
 
-> Experimental contributor checkpoint notice
->
-> These artifacts are not an official Autoware release. They are experimental checkpoints published for contributors to reproduce evaluation results, inspect model behavior, and continue research. They are not release-qualified or safety-qualified models.
+All values are ADE / FDE in metres for a single predicted trajectory; lower is better. "Lateral" and "longitudinal" are the mean absolute errors across the valid horizon.
 
-This repository contains four trajectory-planning checkpoints from the AutoE2E project. These are the exact PyTorch training checkpoints registered in MLflow, including model, optimizer, scheduler, configuration, metrics, and training state.
+### KITScenes-Multimodal Test (official split, cameras only)
 
-## Checkpoints
+206 scenes, 23,690 samples. The model receives cameras and ego-motion only; map and route inputs are absent. Ground truth comes from the official KITScenes `poses.txt`. Neither checkpoint saw these scenes during training.
 
-| Path | Training stage | Epoch | MLflow version | SHA-256 |
-|---|---|---:|---:|---|
-| `models/nuplan-epoch-4/checkpoint.pt` | nuPlan trajectory and route | 4 | 64 | `ed00e072471aae51b4cf48fa54c3fd9c03c61e0fd6e2978829cd9068dfd6da68` |
-| `models/nuplan-epoch-5/checkpoint.pt` | nuPlan trajectory and route | 5 | 65 | `ca8b43d7a777d6fd9195bb253d1452f3cd645f7aab3bd0e36b1f74ffb31df29b` |
-| `models/kitscenes-epoch-5/checkpoint.pt` | KITScenes fine-tuning | 5 | 66 | `120a21639d9767d512eea645be0a83b01476c9e0366a8b957ac51b7ead1bd762` |
-| `models/kitscenes-epoch-7/checkpoint.pt` | KITScenes fine-tuning | 7 | 67 | `a1e6b1621018740485b3e1e43dda23713d913667eb39a65b77fcbf895dcbcefd` |
+| Model | 1 s | 2 s | 3 s | 5 s | Lateral | Longitudinal |
+|---|---:|---:|---:|---:|---:|---:|
+| nuPlan Epoch 5 (zero-shot) | 0.160 / 0.313 | **0.398 / 0.973** | **0.792 / 2.162** | 2.112 / 6.175 | **1.058** | 1.550 |
+| KITScenes Epoch 5 | **0.146 / 0.311** | 0.412 / 1.045 | 0.826 / 2.220 | **2.104 / 5.942** | 1.204 | **1.405** |
 
-All checkpoints use the `bevformer_v2_t8_split_navigation_v5` configuration. The camera BEV starts from the official BEVFormer V2 R50 T8 initialization and is frozen during trajectory stages. World Model and Reasoning branches are disabled.
+### KITScenes-Multimodal Validation (cameras + HD map + route)
 
-## KITScenes Val v3.5
+117 scenes, 11,035 samples. The route is an oracle reconstructed from the logged future trajectory, so these numbers measure route-conditioned driving rather than online route planning. Ground truth is the `trajectory_xy_m` field of the AutoE2E KITScenes v3.5 shards published under [`datasets/kitscenes-val`](./datasets/kitscenes-val/README.md). That field was produced by an older coordinate conversion; rescoring against the official `poses.txt` is tracked in issue #210.
 
-KITScenes Val v3.5 contains 11,035 samples. Evaluation uses Camera, HD Map, and an oracle Route reconstructed retrospectively from the logged future trajectory.
+| Model | 1 s | 2 s | 3 s | 5 s | Lateral | Longitudinal |
+|---|---:|---:|---:|---:|---:|---:|
+| nuPlan Epoch 5 (zero-shot) | 0.195 / 0.378 | 0.472 / 1.133 | 0.915 / 2.423 | 2.295 / 6.388 | 1.191 | 1.663 |
+| KITScenes Epoch 5 | **0.147 / 0.285** | **0.373 / 0.924** | **0.745 / 2.020** | **1.941 / 5.564** | **0.984** | **1.402** |
 
-Each horizon value is ADE / FDE in meters.
+KITScenes labels stop at 5 s, so the 6.4 s horizon cannot be scored on KITScenes.
 
-| MLflow version | Model | 1 s | 2 s | 3 s | 5 s | Lateral error | Longitudinal error |
-|---:|---|---:|---:|---:|---:|---:|---:|
-| 64 | nuPlan Epoch 4 | 0.2030 / 0.4066 | 0.5018 / 1.1935 | 0.9503 / 2.4508 | 2.2919 / 6.2272 | 1.2082 | 1.6448 |
-| 65 | nuPlan Epoch 5 | 0.1951 / 0.3779 | 0.4723 / 1.1331 | 0.9147 / 2.4226 | 2.2951 / 6.3885 | 1.1911 | 1.6633 |
-| 66 | KITScenes Epoch 5 | 0.1472 / 0.2850 | 0.3729 / 0.9239 | 0.7449 / 2.0196 | 1.9405 / 5.5645 | 0.9844 | 1.4025 |
-| 67 | KITScenes Epoch 7 | 0.1347 / 0.2831 | 0.3886 / 1.0101 | 0.8035 / 2.2127 | 2.0952 / 5.9509 | 1.0220 | 1.5659 |
+### Held-out validation during training (6.4 s)
 
-## KITScenes Test v1.0
+| Model | Validation set | Samples | ADE @ 6.4 s | FDE @ 6.4 s |
+|---|---|---:|---:|---:|
+| nuPlan Epoch 5 | nuPlan held-out training logs | 1,024 | 1.267 | 3.898 |
+| KITScenes Epoch 5 | KITScenes train-dev scenes | 3,820 | 2.633 | 7.557 |
 
-KITScenes Test v1.0 contains 23,690 samples. This evaluation is Camera-only and uses a different scene population from KITScenes Val.
+These sets were used to choose the epoch, so treat them as in-distribution reference values rather than benchmark scores.
 
-Each horizon value is ADE / FDE in meters.
+The per-run reports, including sample counts and dataset digests, are under [`evaluations/overlay-replay-v1`](./evaluations/overlay-replay-v1). They score the predictions stored for the dashboard. The GRU planner is deterministic, so these stored controls are the model's predictions for every sample. Comparisons with other end-to-end models (METEOR, VAD, UniAD, Drive-JEPA, Qwen-Drive, Alpamayo, SimForge) are coordinated in [issue #210](https://github.com/autowarefoundation/auto_e2e/issues/210), where each protocol difference is documented.
 
-| MLflow version | Model | 1 s | 2 s | 3 s | 5 s | Lateral error | Longitudinal error |
-|---:|---|---:|---:|---:|---:|---:|---:|
-| 64 | nuPlan Epoch 4 | 0.1916 / 0.3899 | 0.4817 / 1.1514 | 0.9181 / 2.3895 | 2.2840 / 6.4266 | 1.2167 | 1.6287 |
-| 65 | nuPlan Epoch 5 | 0.1602 / 0.3134 | 0.3979 / 0.9727 | 0.7924 / 2.1621 | 2.1120 / 6.1751 | 1.0584 | 1.5496 |
-| 66 | KITScenes Epoch 5 | 0.1463 / 0.3107 | 0.4121 / 1.0453 | 0.8263 / 2.2199 | 2.1042 / 5.9422 | 1.2044 | 1.4051 |
-| 67 | KITScenes Epoch 7 | 0.1680 / 0.3754 | 0.4923 / 1.2512 | 0.9831 / 2.6242 | 2.4699 / 6.8964 | 1.4730 | 1.6598 |
+## Predictions
 
-## Evaluation scope
+Purple is the logged ground-truth path and green is the AutoE2E prediction, drawn into the cameras and the bird's-eye view. Every frame of these scenes can be played back in the [DataModelConsole dashboard](https://d2itskdqq39tx1.cloudfront.net/).
 
-The external evaluation is a deterministic replay of published control overlays. It does not re-run the model. Val and Test are different populations, so their difference is not evidence of a causal benefit from map or route inputs. The complete index and eight reports are under `evaluations/overlay-replay-v1/`.
+<img src="https://huggingface.co/AutowareFoundation/auto_e2e/resolve/main/assets/prediction_kitscenes_epoch5_val_roundabout.jpg" alt="KITScenes Epoch 5 entering a roundabout" width="100%">
 
-The KITScenes Val route contains oracle maneuver information because it is reconstructed post hoc from the logged future trajectory. Val results must not be interpreted as online route-planning performance.
+*KITScenes Epoch 5 entering a roundabout in Karlsruhe (validation, map and route available).*
 
-## Loading
+<img src="https://huggingface.co/AutowareFoundation/auto_e2e/resolve/main/assets/prediction_nuplan_epoch5_test_night_turn.jpg" alt="nuPlan Epoch 5 turning left at night" width="100%">
+
+*nuPlan Epoch 5, never trained on German roads, turning left at a night-time intersection (KITScenes Test, cameras only).*
+
+<img src="https://huggingface.co/AutowareFoundation/auto_e2e/resolve/main/assets/prediction_nuplan_epoch5_test_forest_turn.jpg" alt="nuPlan Epoch 5 following a curve through a forest" width="100%">
+
+*nuPlan Epoch 5 following a left curve on a forest road (KITScenes Test, cameras only).*
+
+Camera images: [KITScenes-Multimodal](https://kitscenes.com/), CC BY-NC 4.0.
+
+## Quick start
+
+The checkpoints are PyTorch training checkpoints. They load with the AutoE2E source at the revision used to train them:
+
+```bash
+git clone https://github.com/autowarefoundation/auto_e2e.git
+cd auto_e2e
+git checkout 40a75cf8a26b60f6b094125e3924fe0bdb427b02  # experimental/reactive-bev-learning
+pip install -r requirements.txt huggingface_hub
+```
 
 ```python
-import torch
+import json
+import sys
 
-checkpoint = torch.load("checkpoint.pt", map_location="cpu", weights_only=True)
-model.load_state_dict(checkpoint["model_state_dict"], strict=True)
+import torch
+from huggingface_hub import hf_hub_download
+
+sys.path.insert(0, "Model")
+from model_components.auto_e2e import AutoE2E
+from training.reactive_multitask import ReactiveTrainingStage, reactive_model_kwargs
+
+repo_id = "AutowareFoundation/auto_e2e"
+release = json.load(open(hf_hub_download(repo_id, "config.json")))
+entry = release["checkpoints"]["nuplan-epoch-5"]  # or "kitscenes-epoch-5"
+checkpoint_path = hf_hub_download(repo_id, entry["path"])
+
+payload = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+config = payload["config"]
+model = AutoE2E(
+    backbone=config["backbone"],
+    embed_dim=config["embed_dim"],
+    is_pretrained=False,
+    **reactive_model_kwargs(
+        ReactiveTrainingStage(config["training_stage"]),
+        num_views=config["num_views"],
+    ),
+)
+model.load_state_dict(payload["model_state_dict"], strict=True)
+model.eval()
 ```
 
-Use an AutoE2E source revision compatible with `bevformer_v2_t8_split_navigation_v5`. Each model directory contains `metadata.json`. `SHA256SUMS` verifies all published files.
+Each checkpoint also contains the optimizer, scheduler and training state, so it can resume or seed fine-tuning directly. The evaluation entry point for KITScenes is `evaluate_reactive_kitscenes_checkpoint` in [`Platform/pipelines/distributed_training.py`](https://github.com/autowarefoundation/auto_e2e/blob/40a75cf8a26b60f6b094125e3924fe0bdb427b02/Platform/pipelines/distributed_training.py).
+
+## Files
+
+| Path | Contents |
+|---|---|
+| `config.json` | Release description: architecture, input/output contract, checkpoint paths and SHA-256 digests |
+| `models/<id>/checkpoint.pt` | PyTorch checkpoint: `model_state_dict`, `config`, `metrics`, optimizer, scheduler, training state |
+| `models/<id>/metadata.json` | Lineage, training stage, evaluation summary and MLflow registry identifiers |
+| `evaluations/overlay-replay-v1/` | Evaluation reports for the tables above |
+| `datasets/kitscenes-val/` | AutoE2E KITScenes validation shards (v3.5) used by the issue #210 benchmark |
+| `release_manifest.json`, `SHA256SUMS` | Release identity and checksums of every release file |
+
+Verify a download with `sha256sum -c SHA256SUMS`. This release is also available at the Git tag [`v1.0`](https://huggingface.co/AutowareFoundation/auto_e2e/tree/v1.0).
+
+## Training
+
+| | nuPlan Epoch 5 | KITScenes Epoch 5 |
+|---|---|---|
+| Initialization | Official BEVFormer V2 R50 T8 weights | nuPlan Epoch 5 (`ca8b43d7…`) |
+| Data | nuPlan v1.1 train sensor logs, 6 cameras (`CAM_F0, CAM_L0, CAM_R0, CAM_L2, CAM_B0, CAM_R2`) | KITScenes-Multimodal train, 6 cameras, 10 % of scenes held out |
+| Objective | Trajectory imitation and route reconstruction, camera BEV frozen | Same objective, camera BEV frozen |
+| Hardware | 8 GPUs, bf16, global batch 16 | 8 GPUs, bf16, global batch 16 |
 
 ## Intended use and limitations
 
-These checkpoints are experimental research artifacts for contributor evaluation and continued training. They are not an official Autoware release and are not validated for safety-critical deployment. Distribution shift remains relevant.
+AutoE2E v1.0 is a research model for open-loop evaluation, visualization, benchmarking and further training. It is not a certified driving system and must not control a vehicle on public roads.
 
-## License notice
+- The results are open-loop: each prediction is compared with the logged human trajectory. Closed-loop behaviour is not measured here.
+- The camera rigs of nuPlan and KITScenes differ. Other rigs need fine-tuning before the numbers above can be expected.
+- The KITScenes Validation route input is an oracle derived from the future trajectory.
+- Confidence intervals and per-scenario breakdowns are not yet published.
 
-The AutoE2E source code is Apache-2.0. These checkpoint files include parameters initialized from the official BEVFormer V2 R50 T8 checkpoint. The upstream weight license was recorded as `NOASSERTION`, and the pretrained component was trained on nuScenes. Users are responsible for complying with the terms of the upstream weights and datasets. No broader checkpoint license is asserted here.
+## License
 
-## KITScenes validation dataset
+The AutoE2E source code is released under Apache-2.0. These checkpoints are distributed under the following notice instead, because they are derived from third-party weights and datasets:
 
-The unmodified AutoE2E KITScenes validation shards are available under [`datasets/kitscenes-val/v3.5`](./datasets/kitscenes-val/README.md). The source dataset is [KIT-MRT/KITScenes-Multimodal](https://huggingface.co/datasets/KIT-MRT/KITScenes-Multimodal); project and attribution information is available at [kitscenes.com](https://kitscenes.com/). These artifacts remain subject to CC BY-NC 4.0 and the additional terms on the original dataset page.
+- the camera encoder was initialized from the official BEVFormer V2 R50 T8 checkpoint, whose weight license is not asserted upstream and which was trained on nuScenes;
+- the models were trained on nuPlan and KITScenes-Multimodal (CC BY-NC 4.0).
+
+Users are responsible for complying with the terms of the upstream weights and datasets, which include non-commercial restrictions. No broader license is granted for the checkpoint files. The KITScenes validation shards under `datasets/kitscenes-val` remain subject to CC BY-NC 4.0 and the additional terms on the [original dataset page](https://huggingface.co/datasets/KIT-MRT/KITScenes-Multimodal).
+
+## Citation
+
+```bibtex
+@software{autoware_autoe2e_2026,
+  author    = {{The Autoware Foundation}},
+  title     = {AutoE2E: Open End-to-End AI for Self-Driving},
+  year      = {2026},
+  version   = {1.0},
+  url       = {https://github.com/autowarefoundation/auto_e2e},
+  note      = {Checkpoints: https://huggingface.co/AutowareFoundation/auto_e2e}
+}
+```

SHA256SUMS

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 7e576d547291522dd1be153408cea0cdaa280b18db340622b7f57688835a6d0c  models/kitscenes-epoch-5/metadata.json
-a1e6b1621018740485b3e1e43dda23713d913667eb39a65b77fcbf895dcbcefd  models/kitscenes-epoch-7/checkpoint.pt
-f6b4670c41462689c0a7b54c22acbea0c4cbd935d6e369feaea7a963921afb5b  models/kitscenes-epoch-7/metadata.json
-ed00e072471aae51b4cf48fa54c3fd9c03c61e0fd6e2978829cd9068dfd6da68  models/nuplan-epoch-4/checkpoint.pt
-1199152e6c4493cfad16c5dff5053b8edc70db221f70cb57949862b4436eb640  models/nuplan-epoch-4/metadata.json
 ca8b43d7a777d6fd9195bb253d1452f3cd645f7aab3bd0e36b1f74ffb31df29b  models/nuplan-epoch-5/checkpoint.pt
 feaa5ae17652c33bad0a4ec286efe9b487f621ca19e4c4ce428cd9d506967569  models/nuplan-epoch-5/metadata.json
-c1c61990ed5cb0d66b36337d7d5ff67a46c37ddff41bd3266950507d747718ee  release_manifest.json
+b4d56a6b6aa7145ef0f83e7bab12ee0700b90dc455f8fd3f025f33f678b8a6ac  release_manifest.json

config.json

@@ -0,0 +1,122 @@
+{
+  "schema_version": "auto_e2e_release_config_v1",
+  "release": "v1.0",
+  "repository": "AutowareFoundation/auto_e2e",
+  "source": {
+    "repository": "https://github.com/autowarefoundation/auto_e2e",
+    "revision": "40a75cf8a26b60f6b094125e3924fe0bdb427b02",
+    "branch": "experimental/reactive-bev-learning"
+  },
+  "architecture": {
+    "identifier": "bevformer_v2_t8_split_navigation_v5",
+    "branch": "reactive",
+    "backbone": "res_net_50",
+    "embed_dim": 256,
+    "num_views": 6,
+    "camera_image_size": 512,
+    "front_camera_image_size": 1024,
+    "camera_history_frame_offsets": [
+      -7,
+      -6,
+      -5,
+      -4,
+      -3,
+      -2,
+      -1,
+      0
+    ],
+    "camera_history_interval_s": 0.5,
+    "camera_bev": {
+      "architecture": "bevformer_v2_t8",
+      "bev_h": 300,
+      "bev_w": 200,
+      "pc_range": [
+        -60.0,
+        -60.0,
+        -5.0,
+        120.0,
+        60.0,
+        3.0
+      ],
+      "frozen": true
+    },
+    "map_input": {
+      "type": "semantic_raster",
+      "map_channels": 14,
+      "route_channels": 2,
+      "height_px": 450,
+      "width_px": 300,
+      "meters_per_pixel": 0.4,
+      "geometry_id": "autoe2e-bev-450x300-0p4m-v1",
+      "optional": true
+    },
+    "map_fusion": "deformable",
+    "planner": "gru",
+    "egomotion_history": {
+      "steps": 64,
+      "rate_hz": 10,
+      "signals": [
+        "speed_mps",
+        "accel_mps2",
+        "yaw_rate_radps",
+        "curvature_inv_m"
+      ]
+    },
+    "output": {
+      "steps": 64,
+      "rate_hz": 10,
+      "horizon_s": 6.4,
+      "signals": [
+        "longitudinal_acceleration_mps2",
+        "curvature_inv_m"
+      ]
+    },
+    "enable_world_model": false,
+    "enable_reasoning": false,
+    "auxiliary_heads": [
+      "bev_segmentation_8_class",
+      "route_reconstruction"
+    ],
+    "parameter_count": 79906522
+  },
+  "default_checkpoint": "nuplan-epoch-5",
+  "checkpoints": {
+    "nuplan-epoch-5": {
+      "display_name": "nuPlan Epoch 5",
+      "path": "models/nuplan-epoch-5/checkpoint.pt",
+      "metadata_path": "models/nuplan-epoch-5/metadata.json",
+      "sha256": "ca8b43d7a777d6fd9195bb253d1452f3cd645f7aab3bd0e36b1f74ffb31df29b",
+      "size_bytes": 334907944,
+      "epoch": 5,
+      "training_stage": "nuplan_full",
+      "parent": "Official BEVFormer V2 R50 T8 initialization",
+      "physical_camera_order": [
+        "CAM_F0",
+        "CAM_L0",
+        "CAM_R0",
+        "CAM_L2",
+        "CAM_B0",
+        "CAM_R2"
+      ]
+    },
+    "kitscenes-epoch-5": {
+      "display_name": "KITScenes Epoch 5",
+      "path": "models/kitscenes-epoch-5/checkpoint.pt",
+      "metadata_path": "models/kitscenes-epoch-5/metadata.json",
+      "sha256": "120a21639d9767d512eea645be0a83b01476c9e0366a8b957ac51b7ead1bd762",
+      "size_bytes": 334754024,
+      "epoch": 5,
+      "training_stage": "kitscenes_finetune",
+      "parent": "nuplan-epoch-5",
+      "physical_camera_order": [
+        "camera_base_front_center",
+        "camera_ring_front_left",
+        "camera_ring_front_right",
+        "camera_ring_rear_left",
+        "camera_ring_rear",
+        "camera_ring_rear_right"
+      ]
+    }
+  },
+  "license": "other; see README.md#license"
+}

evaluations/overlay-replay-v1/index.json

@@ -1,320 +0,0 @@
-{
-  "created_at": "2026-09-14T09:13:53Z",
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evaluations/overlay-replay-v1/kitscenes-test/kitscenes-epoch-7/report.json

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evaluations/overlay-replay-v1/kitscenes-test/nuplan-epoch-4/report.json

@@ -1,76 +0,0 @@
-{
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evaluations/overlay-replay-v1/kitscenes-val/kitscenes-epoch-7/report.json

@@ -1,76 +0,0 @@
-{
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evaluations/overlay-replay-v1/kitscenes-val/nuplan-epoch-4/report.json

@@ -1,76 +0,0 @@
-{
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models/kitscenes-epoch-7/metadata.json

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-{
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-    "training_data_notice": "The pretrained component was trained on nuScenes, whose terms may impose additional restrictions."
-  },
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-    "KITScenes Val uses an oracle post-hoc route reconstructed from the logged future trajectory.",
-    "KITScenes Val and Test are different scene populations and are not a causal map or route ablation.",
-    "External overlay replay evaluates published controls and does not re-run the model.",
-    "Confidence intervals and per-sample route counterfactuals are not included.",
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-  "repository": "AutowareFoundation/auto_e2e",
-  "schema_version": "auto_e2e_checkpoint_metadata_v1"
-}

models/nuplan-epoch-4/metadata.json

@@ -1,151 +0,0 @@
-{
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-      "config_sha256",
-      "epoch",
-      "metrics",
-      "model_state_dict",
-      "model_state_sha256",
-      "optimizer_state_dict",
-      "scheduler_state_dict",
-      "training_state"
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-    "epoch": 4,
-    "format": "PyTorch training checkpoint",
-    "metrics_sha256": null,
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-        "dataset_version": "v1.0",
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-    "pretrained_component": "Official BEVFormer V2 R50 T8 initialization; weight license recorded as NOASSERTION.",
-    "source_code": "Apache-2.0",
-    "training_data_notice": "The pretrained component was trained on nuScenes, whose terms may impose additional restrictions."
-  },
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-    "Confidence intervals and per-sample route counterfactuals are not included.",
-    "These open-loop checkpoints are not validated for safety-critical deployment."
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-  "lineage": {
-    "parent": "Official BEVFormer V2 R50 T8 initialization",
-    "training_stage": "nuPlan trajectory and route training with frozen camera BEV"
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-    "model_version": "64",
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-  "repository": "AutowareFoundation/auto_e2e",
-  "schema_version": "auto_e2e_checkpoint_metadata_v1"
-}

release_manifest.json

@@ -1,40 +1,65 @@
 {
-  "external_overlay_replay_index_sha256": "be1cec5f77a7a12326070503dbcd1004c63f54450cf1151c763a215816d838bd",
+  "schema_version": "auto_e2e_hf_release_manifest_v2",
+  "repository": "AutowareFoundation/auto_e2e",
+  "release": "v1.0",
+  "published_at": "2026-10-08T07:31:10+00:00",
+  "source_revision": "40a75cf8a26b60f6b094125e3924fe0bdb427b02",
   "models": [
     {
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-      "model_id": "nuplan-epoch-4"
-    },
-    {
+      "model_id": "nuplan-epoch-5",
+      "display_name": "nuPlan Epoch 5",
       "checkpoint_path": "models/nuplan-epoch-5/checkpoint.pt",
       "checkpoint_sha256": "ca8b43d7a777d6fd9195bb253d1452f3cd645f7aab3bd0e36b1f74ffb31df29b",
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-      "model_id": "nuplan-epoch-5"
+      "metadata_sha256": "feaa5ae17652c33bad0a4ec286efe9b487f621ca19e4c4ce428cd9d506967569"
     },
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       "checkpoint_path": "models/kitscenes-epoch-5/checkpoint.pt",
       "checkpoint_sha256": "120a21639d9767d512eea645be0a83b01476c9e0366a8b957ac51b7ead1bd762",
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+      "metadata_sha256": "7e576d547291522dd1be153408cea0cdaa280b18db340622b7f57688835a6d0c"
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-      "mlflow_model_version": "67",
-      "model_id": "kitscenes-epoch-7"
+      "path": "evaluations/overlay-replay-v1/kitscenes-test/nuplan-epoch-5/report.json",
+      "sha256": "fc5b19beea416706461b535314a497d6a2b929d97668e8475638ae04a65221b7"
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+      "sha256": "a8f8777da510b832f49503d959f057df6ab298057542e3ec10fd26bd9cb0bf91"
+    },
+    {
+      "path": "evaluations/overlay-replay-v1/kitscenes-val/nuplan-epoch-5/report.json",
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-  "published_at": "2026-09-16T02:26:06+00:00",
-  "repository": "AutowareFoundation/auto_e2e",
-  "schema_version": "auto_e2e_hf_release_manifest_v1"
+  "assets": [
+    {
+      "path": "assets/autoe2e_architecture.jpg",
+      "sha256": "bd521066df27928e0fb1b531b1dae74528123d7a4f4db6237a7d31b529386813"
+    },
+    {
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+      "sha256": "27d638ff7ec618f9cbeb5ab2b8e24d64b6e8845e9d086687352a47d782d18045"
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+    {
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+      "sha256": "4d137ed27262f20320aedaa5d5d8469d8b0a5e0fbae2c8e464c878415b536614"
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+    {
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+      "sha256": "11f7d76fe2248f574d16d8ec2cea71ae94b0bf18ccc99657761ecbfd9fc7afd4"
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+    {
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+      "sha256": "031c28bc3a055900062e42f4deb3c6e83c194c633108e258941f7423d62f81e8"
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 }