| --- |
| license: cc0-1.0 |
| --- |
| |
| # Vehicle Control Dataset |
|
|
| ## Overview |
|
|
| - **Data type:** Vehicle telemetry (temporal actions + tracking errors) |
| - **Size:** 58,862 rows, 15 columns |
| - **Coverage:** 18 driving days over 191 calendar days (Apr — Nov 2024) |
| - **Format:** Parquet + CSV |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `series_id` | int64 | Session identifier (bag_id → int64) | |
| | `timestamp` | int64 | Nanoseconds since Unix epoch | |
| | `velocity` | float | Vehicle speed (m/s) | |
| | `steering` | float | Steering wheel angle (rad) | |
| | `gas` | float | Gas pedal position [0, 1] | |
| | `brake` | float | Brake pedal position [0, 1] | |
| | `acceleration` | float | Longitudinal acceleration (m/s²) | |
| | `acceleration_x` | float | Acceleration X component (m/s²) | |
| | `acceleration_y` | float | Acceleration Y component (m/s²) | |
| | `lateral_error` | float | Cross-track error (m) | |
| | `longitudinal_error` | float | Along-track error (m) | |
| | `heading_error` | float | Heading error (rad) | |
| | `error_energy` | float | Integrated error energy | |
| | `goal_x` | float | Relative goal position X (m) | |
| | `goal_y` | float | Relative goal position Y (m) | |
|
|
| ### Goal Computation |
|
|
| The goal vector is computed by rotating the tracking error vector from the Frenet frame of the reference trajectory into global coordinates: |
|
|
| ``` |
| θ = heading_v - heading_error |
| |
| goal_x = longitudinal_error · cos(θ) - lateral_error · sin(θ) |
| goal_y = longitudinal_error · sin(θ) + lateral_error · cos(θ) |
| ``` |
|
|
| where `heading_v` is the vehicle heading derived from consecutive GPS measurements (UTM → atan2). |
|
|
| Goals are only available when both errors and GPS heading are non-null. |
|
|
| ## Coverage |
|
|
| | Column | Non-null | % | |
| |---|---|---| |
| | `series_id`, `timestamp` | 58,862 | 100.0% | |
| | `longitudinal_error`, `heading_error`, `error_energy` | 57,691 | 98.0% | |
| | `acceleration` (3 axes) | 56,839 | 96.6% | |
| | `lateral_error` | 47,723 | 81.1% | |
| | `velocity` | 38,895 | 66.1% | |
| | `steering` | 27,075 | 46.0% | |
| | `goal_x`, `goal_y` | 29,735 | 50.5% | |
| | `gas`, `brake` | ~15,894 | 27.0% | |
|
|
| ## Numeric Distribution |
|
|
| | Column | min | median | mean | max | std | |
| |---|---|---|---|---|---| |
| | velocity | 0.0 | 22.65 | 21.78 | 27.53 | 3.43 | |
| | steering | −0.67 | 0.0 | −0.001 | 0.70 | 0.03 | |
| | gas | 0.0 | 0.39 | 0.39 | 0.77 | 0.12 | |
| | brake | 0.0 | 0.0 | 0.002 | 0.80 | 0.02 | |
| | acceleration | −5.81 | 0.01 | 0.002 | 1.99 | 0.16 | |
| | lateral_error | −1.00 | 0.05 | 0.05 | 1.24 | 0.12 | |
| | longitudinal_error | −5.5e5 | −0.0 | −3.9e3 | 1.1e5 | 3.3e4 | |
| | heading_error | −3.09 | −0.002 | 0.004 | 3.12 | 0.19 | |
| | goal_x | −3.5e5 | 0.01 | −21.1 | 0.53 | 2.6e3 | |
| | goal_y | −0.99 | 0.01 | 21.4 | 3.5e5 | 2.6e3 | |
| |
| ## Usage |
| |
| ```python |
| import pandas as pd |
| |
| df = pd.read_parquet("telemetry.parquet") |
| print(f"{len(df)} rows, {df['series_id'].nunique()} sessions") |
| |
| # Train/val/test split by session |
| series_ids = df['series_id'].unique() |
| n = len(series_ids) |
| train_ids = series_ids[:int(n * 0.8)] |
| val_ids = series_ids[int(n * 0.8):int(n * 0.9)] |
| test_ids = series_ids[int(n * 0.9):] |
|
|
| train = df[df['series_id'].isin(train_ids)] |
| val = df[df['series_id'].isin(val_ids)] |
| test = df[df['series_id'].isin(test_ids)] |
| ``` |
| |
| ## License |
| |
| CC0 1.0 Universal — No rights reserved. |
| |
| --- |
| |
| Source: Fleet of autonomous vehicles, real driving sessions |
| |
| Sensor: GPS (NovAtel), IMU, CAN bus, control evaluator module |
| |
| Coverage: 58,862 timesteps, 33 sessions, 191 calendar days (18 driving days) |
| |
| Format: Parquet + CSV |
| |
| Total file size: 3.2 MB (Parquet) + 11.9 MB (CSV) |
| |
