README.md

---
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: seg_front
    dtype: image
  - name: lidar
    list:
      list: float32
  - name: boxes
    list:
      list: float32
  - name: box_labels
    list: string
  - 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: 298274262201
    num_examples: 67000
  - name: validation
    num_bytes: 35503432435.4
    num_examples: 8400
  - name: test
    num_bytes: 31770625008.6
    num_examples: 7200
  download_size: 361766155632
  dataset_size: 365548319645
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- object-detection
- image-classification
- image-segmentation
- depth-estimation
- video-classification
- any-to-any
- image-to-text
- reinforcement-learning
language:
- en
pretty_name: CARLA Autopilot Multimodal Dataset
size_categories:
- 10K<n<100K
---

# CARLA Autopilot Multimodal Dataset

This dataset contains synchronized multimodal driving data collected in the [CARLA simulator](https://carla.org/) using the autopilot feature. It provides RGB images from multiple cameras, semantic segmentation, LiDAR point clouds, 2D bounding boxes, and ego-vehicle state/control signals across varied weather, maps, and traffic densities.

The dataset is designed for research in **autonomous driving**, **sensor fusion**, **imitation learning**, and **self-driving evaluation**.

---

## Dataset Summary

- **Runs**: 30 autopilot runs
- **Sensors**: 
  - RGB cameras: front, front-left, front-right, rear (800×600, fov=90°)  
  - Semantic segmentation: front (raw + colorized)  
  - LiDAR: 32-channel ray-cast, 20 Hz, 80 m range  
  - Collision sensor for impact logs  
- **Annotations**: 2D bounding boxes and class labels (vehicles, pedestrians) w.r.t front camera  
- **Ego states**: position, rotation, velocity, control (throttle/steer/brake), speed (km/h)  
- **Environment**: varied weather, time-of-day (sun altitude), NPC traffic (vehicles + pedestrians)  

**Splits**  
- Train: 67,000 frames  
- Validation: 8,400 frames  
- Test: 7,200 frames  
- Total size: ~365 GB

---

## Relation to Previous Versions

This dataset, **CARLA Autopilot Multimodal Dataset**, is an extension of the earlier 
[CARLA Autopilot Image Dataset](https://huggingface.co/datasets/immanuelpeter/carla-autopilot-images).

- **Previous version (`carla-autopilot-images`)**:  
  Contained synchronized RGB camera views (front, front-left, front-right, rear) with ego-vehicle states, controls, and environment metadata.

- **Current version (`carla-autopilot-multimodal-dataset`)**:  
  Adds **new sensor modalities and richer annotations**, including:  
  - Semantic segmentation (front view)  
  - LiDAR point clouds  
  - 2D bounding boxes and labels (vehicles, pedestrians)  
  - Expanded metadata (collisions, weather difficulty, quality metrics)  

In short, `v2` augments the original dataset with **multimodal signals for perception + sensor fusion research**, 
while retaining full compatibility with the core camera + state data from `v1`.

---

## Features

Each sample contains:

- `run_id` (string): Identifier for the simulation run  
- `frame` (int): Frame number  
- `timestamp` (float): Relative timestamp (s)  
- `image_front`, `image_front_left`, `image_front_right`, `image_rear` (images): RGB views  
- `seg_front` (image): Semantic segmentation (front view)  
- `lidar` (list[list[float32]]): LiDAR point cloud (x, y, z, intensity)  
- `boxes` (list[list[float32]]): 2D bounding boxes in `[xmin, ymin, xmax, ymax]` format  
- `box_labels` (list[string]): Class labels for bounding boxes  
- `location_{x,y,z}` (float): Ego position in world coords  
- `rotation_{pitch,yaw,roll}` (float): Ego rotation  
- `velocity_{x,y,z}` (float): Ego velocity (m/s)  
- `speed_kmh` (float): Ego speed (km/h)  
- `throttle`, `steer`, `brake` (float): Control inputs  
- `nearby_vehicles_50m`, `total_npc_vehicles`, `total_npc_walkers` (int): Traffic counts  
- `map_name` (string): CARLA map used  
- `weather_*` (float): Weather conditions (cloudiness, precipitation, fog, sun altitude)  
- `vehicles_spawned`, `walkers_spawned` (int): Number of NPCs  
- `duration_seconds` (int): Total run length in seconds  

---

## Example Usage

```python
from datasets import load_dataset

ds = load_dataset("immanuelpeter/carla-autopilot-multimodal-dataset", split="train")
sample = ds[0]

# Access RGB image and LiDAR
front_img = sample["image_front"]
lidar = sample["lidar"]
boxes = sample["boxes"]
````

---

## Collection Process

Data was collected using a custom CARLA Python script that:

* Spawns an ego vehicle with autopilot enabled
* Spawns configurable NPC vehicles and pedestrians
* Randomizes weather and lighting conditions per run
* Synchronizes all sensors and saves every *N*-th frame
* Records vehicle state, control signals, collisions, and environment statistics

All sensors operate in synchronous mode for frame alignment.

---

## Intended Use

* Training and benchmarking multimodal self-driving models
* Research on sensor fusion, perception, and planning
* Imitation learning from autopilot trajectories
* Evaluation under diverse weather and traffic conditions

## 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}
}
```

<!-- ## Citation

If you use this dataset, please cite:

```
@dataset{yourname2025carlaautopilot,
  author    = {Your Name},
  title     = {CARLA Autopilot Multimodal Dataset},
  year      = {2025},
  howpublished = {\url{https://huggingface.co/datasets/your-username/carla-autopilot-multimodal-dataset}}
}
``` -->