metadata
license: mit
task_categories:
- robotics
- image-to-text
tags:
- autonomous-driving
- carla
- simlingo
- behavioral-cloning
size_categories:
- 100K<n<1M
SimLingo CARLA Dataset (Raw, 4Hz)
Raw driving data from CARLA simulator. No transformations or derived fields - all original measurements preserved as-is.
Dataset Summary
- Source: SimLingo (CVPR 2025)
- Scale: 228,757 frames (23 shards)
- Frame Rate: 4 FPS
- Resolution: 1024x512 RGB
- Routes: Complete driving episodes (routes never split across shards)
Column Schema
Core Fields
| Column |
Type |
Description |
route_id |
string |
Route identifier |
frame_idx |
int32 |
Frame index within route |
image |
bytes |
Original JPEG image bytes |
Control Signals (Raw)
| Column |
Type |
Description |
steer |
float32 |
Steering [-1, 1] |
throttle |
float32 |
Throttle [0, 1] |
brake |
bool |
Brake applied |
Vehicle State
| Column |
Type |
Description |
speed |
float32 |
Current speed (m/s) |
target_speed |
float32 |
Target speed |
speed_limit |
float32 |
Speed limit |
theta |
float32 |
Heading angle |
angle |
float32 |
Angle to target |
Navigation
| Column |
Type |
Description |
command |
int32 |
Navigation command |
next_command |
int32 |
Next navigation command |
pos_global |
string (JSON) |
Global position [x, y] |
target_point |
string (JSON) |
Target point |
target_point_next |
string (JSON) |
Next target point |
aim_wp |
string (JSON) |
Aim waypoint |
route |
string (JSON) |
Planned route waypoints |
route_original |
string (JSON) |
Original route waypoints |
changed_route |
bool |
Route was changed |
Hazards & Environment
| Column |
Type |
Description |
junction |
bool |
In junction |
vehicle_hazard |
bool |
Vehicle hazard detected |
vehicle_affecting_id |
int32 |
ID of affecting vehicle |
walker_hazard |
bool |
Pedestrian hazard |
walker_affecting_id |
int32 |
ID of affecting pedestrian |
light_hazard |
bool |
Traffic light hazard |
stop_sign_hazard |
bool |
Stop sign hazard |
stop_sign_close |
bool |
Stop sign nearby |
walker_close |
bool |
Pedestrian nearby |
walker_close_id |
int32 |
ID of nearby pedestrian |
speed_reduced_by_obj_type |
string |
Object type causing speed reduction |
speed_reduced_by_obj_id |
int32 |
Object ID causing speed reduction |
speed_reduced_by_obj_distance |
float32 |
Distance to speed-reducing object |
control_brake |
bool |
Control brake applied |
Augmentation (from SimLingo)
| Column |
Type |
Description |
augmentation_translation |
float32 |
Translation augmentation |
augmentation_rotation |
float32 |
Rotation augmentation |
Transforms
| Column |
Type |
Description |
ego_matrix |
string (JSON) |
4x4 ego vehicle transform matrix |
boxes |
string (JSON) |
3D bounding boxes for all objects |
Commentary (Optional)
| Column |
Type |
Description |
commentary |
string |
Natural language commentary |
commentary_data |
string (JSON) |
Full commentary object with metadata |
Usage
from datasets import load_dataset
import json
ds = load_dataset("TESS-Computer/carla-simlingo-raw", split="train")
sample = ds[0]
print(sample['route_id'])
print(sample['steer'], sample['throttle'], sample['brake'])
print(sample['speed'])
pos = json.loads(sample['pos_global'])
boxes = json.loads(sample['boxes']) if sample['boxes'] else []
Data Collection
- Simulator: CARLA 0.9.15 (Leaderboard 2.0)
- Expert: PDM-Lite (rule-based, 100% route completion)
- Scenarios: Single-scenario routes with random weather
- Towns: Towns 1-13
Citation
@inproceedings{renz2025simlingo,
title={SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment},
author={Renz, Katrin and Chen, Long and Arani, Elahe and Sinavski, Oleg},
booktitle={CVPR},
year={2025},
}
License
MIT (dataset processing code). Original data subject to SimLingo and CARLA licenses.
