---
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)