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README.md

@@ -1,11 +1,8 @@
 ---
 license: apache-2.0
 task_categories:
-- autonomous-navigation
-- computer-vision
 - robotics
 tags:
-- autonomous-driving
 - deepracer
 - carla-simulator
 - urjc
@@ -14,13 +11,20 @@ pretty_name: URJC-DeepRacer Autonomous Driving Dataset
 size_categories:
 - 10K<n<100K
 
-# Dataset Viewer Configuration
 dataset_info:
   features:
-    - name: file_name
+    - name: image_path
       dtype: image
     - name: mask_path
       dtype: image
+    - name: maneuver
+      dtype:
+        class_label:
+          names:
+            '0': unknown
+            '1': left_curve
+            '2': straight
+            '3': right_curve
     - name: timestamp
       dtype: float64
     - name: throttle
@@ -33,15 +37,9 @@ dataset_info:
       dtype: float32
     - name: heading
       dtype: float64
-    - name: estado
-      dtype: int32
     - name: experiment_id
       dtype: string
-  splits:
-    - name: train
-      path: training/metadata.jsonl
-    - name: test
-      path: testing/metadata.jsonl
+
 ---
 
 # URJC-DeepRacer: Autonomous Driving Dataset
@@ -50,7 +48,7 @@ This dataset was generated by Sergio Robledo as part of the **URJC-DeepRacer** p
 
 Data was collected using the **CARLA Simulator**, featuring DeepRacer vehicle models and custom-designed racing environments.
 
-## 📊 Dataset Overview
+## =Ê Dataset Overview
 
 Each entry provides a synchronized capture of the front-facing RGB camera, its corresponding semantic segmentation mask, and the vehicle's real-time telemetry.
 
@@ -65,7 +63,7 @@ Each entry provides a synchronized capture of the front-facing RGB camera, its c
 - **experiment_id**: Unique identifier for the simulation session/map.
 - **estado**: Agent state indicator during collection.
 
-## 🚀 Getting Started
+## =€ Getting Started
 
 You can load this dataset directly using the Hugging Face `datasets` library:
 
@@ -83,12 +81,15 @@ sample['file_name'].show()
 sample['mask_path'].show()
 
 print(f"Speed: {sample['speed']} m/s | Steer: {sample['steer']}")
+```
+
 
-## 🛠️ Data Collection Methodology
+## =à Data Collection Methodology
 The data is organized by individual experiments to facilitate the study of various scenarios, including different weather conditions, maps, and traffic densities. Using the jsonl format ensures efficient and scalable data streaming.
 
-## 🎓 Credits & Affiliation
+## <“ Credits & Affiliation
 This dataset is maintained by URJC DeepRacer developers as part of ongoing autonomous driving research.
 
 
 
+