metadata
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
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)