What is AI in the context of autonomous vehicles?
In autonomous vehicles, AI is an intelligent software system responsible for perception, decision-making, and control on behalf of humans. The primary goal is safe, accurate, and fast driving in real time.
Machine Learning (ML) and Deep Learning (DL) are key.
The heart of AI in self-driving cars is the use of machine learning (ML) techniques, particularly deep learning (DL), which uses complex, multi-layered neural networks to learn from massive amounts of data.
1. Perception and Understanding of the Environment
This is the stage where AI must learn to "see" the world like humans, but faster and more accurately:
- Input: The system receives raw data from surrounding sensors (cameras, LiDAR, radar).
- Deep Learning Training: Developers use Deep Neural Networks to train the AI to recognize and classify objects in the data:
- Objects: Other cars, pedestrians, bicycles, trees, animals
- Traffic Signs and Signals: Learn the shape and color of stop signs, speed limits, and traffic light status.
- Lane Lines: Accurately identify and follow lane lines. Even in blurry road conditions,
2. Prediction
Autonomous vehicles need to know not only where objects are, but also predict what those objects will do next. ML plays a key role in predicting probabilities:
- Motion Analysis: AI analyzes the movement patterns of the vehicle ahead (e.g., whether it's braking or changing lanes) and pedestrians (e.g., whether it's about to step out of the road).
- Learning from Historical Data: Machine learning learns from millions of miles of real-world driving data to build models of various road behaviors, enabling the vehicle to prepare for unexpected situations.
3. Decision Making
After the system understands and predicts, the AI decides what to do next in real time.
- Path Planning: The system calculates the optimal path within the lane and avoids objects.
- Control Command: Commands are translated into robotic control signals, such as:
- Steering Command: Turn slightly to maintain distance from the curb or make an emergency evasive maneuver.
- Acceleration/Brake Command: Accelerate, decelerate, or apply emergency braking (AEB).
Continuous Learning Cycle
The difference between autonomous cars and conventional cars is that the AI can constantly learn and improve itself (Learn from Data).
- Data Collection: When autonomous cars are on the road, they collect data on various driving situations (e.g., weather, traffic congestion, and human driving).
- Over-the-Air (OTA) Software Updates: This data is sent back to the development center to train the AI model to be more intelligent. When new software updates are released, every car is updated via the internet, allowing every car to learn from the experience of other cars on the road around the world.
Machine learning and deep learning are what transform cars from "command-following machines" into "thinking and decision-making vehicles," which is the core behind today's autonomous driving capabilities.
Key Technologies:
- AI, Machine Learning, Deep Learning, Artificial Intelligence
System Operations:
- Automotive Software, Data Processing, Autonomous Driving
Broad Topics:
- Autonomous Cars, Automotive Technology, Neural Networks
AI, Machine Learning, Deep Learning, Autonomous Cars, Automotive Software, Autonomous Driving
This image is: AI is the brain of the car (Concept). This image shows an autonomous car with the AI brain or processing chip in the center of the car and lines connecting to various parts such as sensors (cameras, LiDAR, radar) and control systems (steering, brakes) to convey that AI is the decision-making center.
