Autonomous vehicles rely heavily on deep learning to process sensor data, make real-time decisions, and navigate complex environments. Below are key aspects of this technology:
Core Applications
- Object Detection: CNNs identify pedestrians, vehicles, and obstacles (🤖🚦).
- Semantic Segmentation: Maps pixel-level scene understanding (🗺️🔍).
- Motion Prediction: RNNs forecast object trajectories (🚗📊).
- End-to-End Learning: Neural networks directly map sensor inputs to control outputs (🔧🧠).
Key Challenges
- Data Quality: Requires vast annotated datasets (📊🛠️).
- Real-Time Processing: High computational demands (⚡💻).
- Safety & Reliability: Ensuring robustness in edge cases (🛡️⚠️).