README.md

metadata

dataset_info:
  features:
    - name: run_id
      dtype: string
    - name: frame
      dtype: int32
    - name: timestamp
      dtype: float32
    - name: image_front
      dtype: image
    - name: image_front_left
      dtype: image
    - name: image_front_right
      dtype: image
    - name: image_rear
      dtype: image
    - name: location_x
      dtype: float32
    - name: location_y
      dtype: float32
    - name: location_z
      dtype: float32
    - name: rotation_pitch
      dtype: float32
    - name: rotation_yaw
      dtype: float32
    - name: rotation_roll
      dtype: float32
    - name: velocity_x
      dtype: float32
    - name: velocity_y
      dtype: float32
    - name: velocity_z
      dtype: float32
    - name: speed_kmh
      dtype: float32
    - name: throttle
      dtype: float32
    - name: steer
      dtype: float32
    - name: brake
      dtype: float32
    - name: nearby_vehicles_50m
      dtype: int32
    - name: total_npc_vehicles
      dtype: int32
    - name: total_npc_walkers
      dtype: int32
    - name: map_name
      dtype: string
    - name: weather_cloudiness
      dtype: float32
    - name: weather_precipitation
      dtype: float32
    - name: weather_fog_density
      dtype: float32
    - name: weather_sun_altitude
      dtype: float32
    - name: vehicles_spawned
      dtype: int32
    - name: walkers_spawned
      dtype: int32
    - name: duration_seconds
      dtype: int32
  splits:
    - name: train
      num_bytes: 155077419480.6
      num_examples: 56200
    - name: validation
      num_bytes: 14948709540
      num_examples: 4800
    - name: test
      num_bytes: 17602075134
      num_examples: 7200
  download_size: 189226141844
  dataset_size: 187628204154.6
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: mit
ta:
  - vision-to-control
  - imitation-learning
  - autonomous-driving
  - multimodal
  - computer-vision
  - reinforcement-learning
language:
  - en
pretty_name: CARLA Autopilot Image Dataset
size_categories:
  - 10K<n<100K
task_categories:
  - image-feature-extraction
  - any-to-any
  - reinforcement-learning

CARLA Autopilot Images Dataset

Note: A newer, extended version of this dataset is available.
🤗 CARLA Autopilot Multimodal Dataset 🤗
It includes semantic segmentation, LiDAR, 2D bounding boxes, and additional environment metadata.
Use it if your research requires multimodal signals beyond the RGB images and vehicle state/control data provided here.

This dataset contains autonomous driving data collected from CARLA simulator using autopilot.

Dataset Structure

• Train/val/test split is by run, not by frame, to ensure generalization
• Total train samples: 56.2K
• Total val samples: 4.8K
• Total test samples: 7.2K
• Runs processed: 24

Features

Images

Multiple camera views are available depending on the run configuration:

• image_front: Front-facing camera view
• image_rear: Rear-facing camera view
• image_front_left: Front-left camera view
• image_front_right: Front-right camera view

Vehicle State

• Position: location_x, location_y, location_z
• Orientation: rotation_pitch, rotation_yaw, rotation_roll
• Velocity: velocity_x, velocity_y, velocity_z, speed_kmh

Vehicle Controls (Targets)

• throttle: Throttle input [0.0, 1.0]
• steer: Steering input [-1.0, 1.0]
• brake: Brake input [0.0, 1.0]

Environment

• Traffic density information
• Weather conditions
• Map information

Usage

from datasets import load_dataset

dataset = load_dataset("immanuelpeter/carla-autopilot-images")
train_dataset = dataset["train"]
val_dataset = dataset["validation"]
test_dataset = dataset["test"]

Citation

If you use this dataset, please cite the CARLA simulator:

@inproceedings{Dosovitskiy17,
  title = {CARLA: An Open Urban Driving Simulator},
  author = {Alexey Dosovitskiy and German Ros and Felipe Codevilla and Antonio Lopez and Vladlen Koltun},
  booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
  pages = {1--16},
  year = {2017}
}