Update README.md

Refined GPT response

README.md

@@ -75,7 +75,7 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 298274262201.0
+    num_bytes: 298274262201
     num_examples: 67000
   - name: validation
     num_bytes: 35503432435.4
@@ -84,7 +84,7 @@ dataset_info:
     num_bytes: 31770625008.6
     num_examples: 7200
   download_size: 361766155632
-  dataset_size: 365548319645.0
+  dataset_size: 365548319645
 configs:
 - config_name: default
   data_files:
@@ -94,4 +94,142 @@ configs:
     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/your-username/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:
+
+```
+@dataset{yourname2025carlaautopilot,
+  author    = {Your Name},
+  title     = {CARLA Autopilot Multimodal Dataset},
+  year      = {2025},
+  howpublished = {\url{https://huggingface.co/datasets/your-username/carla-autopilot-multimodal-dataset}}
+}
+``` -->