Add Hyperion Drive sample (10K event chains) with README, SCHEMA, parquet, JSONL

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+hyperion_drive_sample.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,156 @@
+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - synthetic
+  - autonomous-vehicles
+  - adas
+  - automotive
+  - sensor-fusion
+  - can-bus
+  - lidar
+  - radar
+  - perception
+  - safety-critical
+  - anomaly-detection
+  - edge-cases
+  - robotics
+pretty_name: Hyperion Drive AV Safety Intelligence Pack
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: hyperion_drive_sample.parquet
+---
+
+# Hyperion Drive AV Safety Intelligence Pack (Sample)
+
+**A synthetic autonomous-vehicle telemetry dataset for ADAS validation, sensor-fusion model training, and edge-case safety research.** Each row is a complete driving event chain — from environmental trigger (black ice, glare, CAN bus glitch) through a cascading perception or control failure — with high-frequency CAN bus signals at each step, failure-class labels, causal-chain annotations, and a safety-critical outcome.
+
+Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real vehicle logs, no real VINs (all VINs prefixed `SYN-VIN-` to disambiguate from real WMIs), no identifiable user data. Safe for model training, regulator evaluation, and public benchmark work.
+
+## What is included
+
+| File | Rows | Format | Purpose |
+|---|---:|---|---|
+| `hyperion_drive_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
+| `hyperion_drive_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |
+
+**This sample:** 10,000 driving event chains, balanced across 4 safety-criticality tiers and 3 failure classes.  
+**Safety-criticality tiers:** `low`, `medium`, `high`, `critical` (~2,500 each)  
+**Failure classes:** `Sensor_Fusion_Conflict`, `Mechanical_Latency_Failure`, `Perception_Hallucination` (~3,300 each)  
+**Vehicle models:** `EV_Sedan_Alpha`, `Autonomous_Shuttle_V2`, `Heavy_Duty_Truck`  
+**Drive modes:** `manual_override`, `autopilot_assisted`, `autonomous`  
+**Environmental chaos profiles:** `Black_Ice`, `Blinding_Glare`, `Intermittent_CAN_Bus_Error`, `Sensor_Ghosting`
+
+## Record structure
+
+Each record is one driving event chain with 7 top-level fields:
+
+| Field | Type | Contents |
+|---|---|---|
+| `schema_version` | string | Pack schema version (`1.0.0-hyperion-drive-sample`) |
+| `event` | struct | `id`, `trace_id`, `timestamp`, `safety_criticality`, `outcome`, `confidence` |
+| `vehicle_context` | struct | `model`, `vin` (synthetic), `drive_mode`, `mileage_km` |
+| `perception_logic` | struct | `failure_class`, `causal_chain[]`, `reaction_time_ms`, `tracked_signals[]` |
+| `can_bus_telemetry` | list<struct> | Ordered CAN/Ethernet bus messages: `timestamp`, `vin`, `event_name`, `sensor_source`, `bus_id`, `signals` (wheel_speed_kph, brake_pedal_pos, steering_angle_deg, sensor_health_pct, camera_confidence_pct, lidar_noise_pct, radar_distance_m, brake_pressure_bar, traction_slip_pct) |
+| `detection_logic` | struct | `signature`, `anomaly_score`, `baseline_deviation` |
+| `simulation` | struct | `synthetic`, `engine`, `chaos_profile` |
+
+See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.
+
+## Why this dataset is useful
+
+Most public autonomous-driving datasets are either massive raw sensor blobs (nuScenes, Waymo Open, KITTI) that require hundreds of GB and deep infrastructure, or very narrow single-signal logs. This pack is shaped around a different need: decision-chain telemetry — the signal-level story of what happened, labeled with failure class and outcome — at a scale you can pull into a notebook in seconds.
+
+- Complete event chains rather than isolated signal windows
+- Balanced safety-criticality tiers across low → critical
+- Causal-chain labels connecting trigger → cascade → outcome
+- Per-step CAN / Ethernet signal snapshots at the moments the failure unfolds
+- Cross-vehicle coverage (passenger EV, autonomous shuttle, heavy-duty truck)
+- Scenario labels across ice, glare, bus glitch, sensor ghosting
+- Compact (parquet + JSONL) so it fits inside CI and rapid prototyping flows
+
+## Typical use cases
+
+- ADAS and AV validation pipelines
+- Sensor-fusion conflict detection
+- Anomaly detection on CAN bus signals
+- Failure-class classification models
+- Reaction-time and control-delay modeling
+- LLM fine-tuning on automotive incident narratives
+- Scenario-mining prototypes for fleet debugging
+- Dashboard / BI template development for AV observability
+
+## Quick start
+
+```python
+import pandas as pd
+import pyarrow.parquet as pq
+
+df = pq.read_table("hyperion_drive_sample.parquet").to_pandas()
+
+# Safety-criticality distribution (balanced)
+print(df["event"].apply(lambda e: e["safety_criticality"]).value_counts())
+
+# Reaction-time by failure class
+df["failure"] = df["perception_logic"].apply(lambda p: p["failure_class"])
+df["reaction_ms"] = df["perception_logic"].apply(lambda p: p["reaction_time_ms"])
+print(df.groupby("failure")["reaction_ms"].mean().round(1))
+
+# Drive-mode vs outcome cross-tab
+df["drive_mode"] = df["vehicle_context"].apply(lambda v: v["drive_mode"])
+df["outcome"] = df["event"].apply(lambda e: e["outcome"])
+print(pd.crosstab(df["drive_mode"], df["outcome"]))
+```
+
+Streaming form:
+
+```python
+import json
+
+with open("hyperion_drive_sample.jsonl") as f:
+    for line in f:
+        chain = json.loads(line)
+        # one driving event chain per line
+```
+
+## Responsible use
+
+This dataset is intended for **research, model training, and safety-evaluation** use cases around autonomous vehicles and ADAS systems. It contains synthesized CAN bus signals, synthetic VINs, and scenario labels — it does **not** contain real vehicle logs, real VINs, real user data, or identifiable information of any kind. Models trained on this data must be independently validated against real fleet telemetry under appropriate safety-assurance review before any deployment decisions.
+
+## License
+
+Released under **CC BY 4.0**. Use freely for research, ADAS prototyping, education, and commercial development with attribution.
+
+## Get the full pack
+
+This Hugging Face repo is a **10K-event-chain sample**. The production pack scales to 1M+ chains with finer-grained signal sampling (ms-level CAN traces), expanded scenario coverage (rain, snow, construction zones, V2X signal loss, cyberattacks on bus), additional vehicle classes (motorcycle, delivery robot, transit bus), per-sensor noise models, ROS-bag and CAN DBC-aligned delivery, and buyer-specific variants.
+
+**Self-serve (Stripe checkout):**
+- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
+
+**Full pack + enterprise scope:**
+- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.
+
+**Procurement catalog:**
+- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
+
+## Citation
+
+```bibtex
+@dataset{solstice_hyperion_drive_pack_2026,
+  title        = {Hyperion Drive AV Safety Intelligence Pack (Sample)},
+  author       = {SolsticeAI},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/solsticestudioai/hyperion-drive-pack}
+}
+```

SCHEMA.md

@@ -0,0 +1,103 @@
+# Hyperion Drive AV Safety Intelligence Pack — Schema
+
+One row = one complete driving event chain. All records share the same seven top-level fields.
+
+Schema version: `1.0.0-hyperion-drive-sample`
+
+## Top-level fields
+
+### `schema_version` — string
+Schema identifier. Constant within a sample release.
+
+### `event` — struct
+Identifier fields and the overall safety outcome for the chain.
+
+| Field | Type | Notes |
+|---|---|---|
+| `id` | string | Stable event identifier, e.g., `HYPERION-100000`. |
+| `trace_id` | string (UUID) | Cross-links telemetry messages within the chain. |
+| `timestamp` | string (ISO-8601) | Chain anchor time. |
+| `safety_criticality` | string | `low`, `medium`, `high`, `critical`. |
+| `outcome` | string | `near_miss`, `minor_impact`, `emergency_braking_engaged`, `catastrophic_failure`. |
+| `confidence` | double | 0–1 confidence of the outcome label. |
+
+### `vehicle_context` — struct
+Synthetic vehicle identity and operational context.
+
+| Field | Type | Notes |
+|---|---|---|
+| `model` | string | `EV_Sedan_Alpha`, `Autonomous_Shuttle_V2`, `Heavy_Duty_Truck`. |
+| `vin` | string | Synthetic VIN, prefixed with `SYN-VIN-`. Not a real 17-char registered VIN. |
+| `drive_mode` | string | `manual_override`, `autopilot_assisted`, `autonomous`. |
+| `mileage_km` | int | Vehicle odometer reading in km. |
+
+### `perception_logic` — struct
+Driver of this chain — which perception or control subsystem failed, and how fast.
+
+| Field | Type | Notes |
+|---|---|---|
+| `failure_class` | string | `Sensor_Fusion_Conflict`, `Mechanical_Latency_Failure`, `Perception_Hallucination`. |
+| `causal_chain` | list<string> | Ordered event labels (e.g., `ICE_DETECTED`, `TRACTION_CONTROL_ENGAGED`, `BRAKE_FLUID_PRESSURE_LAG`, `ABS_PUMP_STALL`). Each label appears as the `event_name` of one `can_bus_telemetry` entry. |
+| `reaction_time_ms` | int | End-to-end reaction time (ms). |
+| `tracked_signals` | list<string> | Which signal fields drive this failure (e.g., `wheel_speed_kph`, `brake_pressure_bar`). |
+
+### `can_bus_telemetry` — list<struct>
+Ordered CAN / Ethernet bus messages observed during the chain.
+
+Message struct:
+
+| Field | Type | Notes |
+|---|---|---|
+| `timestamp` | string (ISO-8601) | Message timestamp. |
+| `vin` | string | Synthetic VIN (`SYN-VIN-*`). Matches `vehicle_context.vin`. |
+| `event_name` | string | Matches one item in `perception_logic.causal_chain`. |
+| `sensor_source` | string | Originating sensor (e.g., `Radar_Long_Range`, `Ultrasonic_Rear`, `Camera_Left_30`, `LIDAR_Roof`). |
+| `bus_id` | string | Bus identifier (`CAN_0`, `CAN_1`, `ETH_INTERNAL`). |
+| `signals.wheel_speed_kph` | double | Wheel speed (kph). |
+| `signals.brake_pedal_pos` | double | Brake pedal position (0–100). |
+| `signals.steering_angle_deg` | double | Steering angle (degrees). |
+| `signals.sensor_health_pct` | int | Composite sensor health (0–100). |
+| `signals.camera_confidence_pct` | int | Camera detection confidence (0–100). |
+| `signals.lidar_noise_pct` | int | LiDAR noise level (0–100). |
+| `signals.radar_distance_m` | double | Closest radar return, meters. |
+| `signals.brake_pressure_bar` | double | Brake line pressure (bar). |
+| `signals.traction_slip_pct` | double | Traction slip percentage. |
+
+### `detection_logic` — struct
+Anomaly-signature metadata describing why this chain is flagged.
+
+| Field | Type | Notes |
+|---|---|---|
+| `signature` | string | Human-readable signature (e.g., `Safety Violation: Mechanical_Latency_Failure detected`). |
+| `anomaly_score` | double | 0–1. Higher = more anomalous. |
+| `baseline_deviation` | string | Short English description of the deviation. |
+
+### `simulation` — struct
+Simulation engine provenance.
+
+| Field | Type | Notes |
+|---|---|---|
+| `synthetic` | bool | Always `true`. |
+| `engine` | string | Simulation engine label (`hyperion_physics_sim_v1`). |
+| `chaos_profile` | string | `Black_Ice`, `Blinding_Glare`, `Intermittent_CAN_Bus_Error`, `Sensor_Ghosting`. |
+
+## Distribution of this sample
+
+- 10,000 event chains total.
+- Safety criticality: balanced (~2,500 per class).
+- Failure class: balanced across 3 classes (~3,300 each).
+- Vehicle model: balanced across 3 models (~3,300 each).
+- Drive mode: balanced across 3 modes (~3,300 each).
+- Chaos profile: balanced across 4 profiles (~2,500 each).
+
+## Sanitization notes
+
+- Internal identifier prefix (`SIMA-V4-AUTO-*`) has been normalized to `HYPERION-*`.
+- Internal physics engine code name (`Hyperion-Physics-V2`) has been normalized to `hyperion_physics_sim_v1`.
+- VIN values have been replaced with a `SYN-VIN-*` prefix so they cannot be confused with real WMIs assigned to specific manufacturers.
+- Vehicle model names are abstract placeholders (`EV_Sedan_Alpha`, `Autonomous_Shuttle_V2`, `Heavy_Duty_Truck`), not real product names.
+- No real vehicle logs, real VINs, real user data, or identifiable information are present.
+
+## Relationship to the full pack
+
+The production pack scales to 1M+ event chains with finer-grained signal sampling (ms-level CAN traces), expanded scenario coverage (rain, snow, construction zones, V2X signal loss, bus cyberattacks), additional vehicle classes (motorcycle, delivery robot, transit bus), per-sensor noise models, and ROS-bag / CAN DBC-aligned delivery. See the pack card for commercial access.

hyperion_drive_sample.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65ca1063368e3cc36a19f5d11f8480525341516bbf274e1eb95caab507efef2a
+size 26549477

hyperion_drive_sample.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07b894e2b2a458ba9e5d8f2983966381b91b007cc47d3954eb9092363e5d2523
+size 1786732