**What Role Will Artificial Intelligence Play in Driverless Technology?**
With the rapid advancement of modern technology, digitalization, informatization, and intelligence are becoming more deeply embedded in every aspect of human life. What once seemed like science fiction—driverless cars—is now becoming a reality. In the near future, we will witness the emergence of smart, autonomous vehicles on our roads. These cars integrate various high-tech systems, significantly improving performance, comfort, and safety. At the core of these vehicles is a highly intelligent computer that can collect and process data from both the car’s internal systems and its surroundings, enabling efficient decision-making and control for automatic driving.
**The Technical Principle Behind Autonomous Driving**
When a driver sets a destination on the car's electronic map, the system automatically imports this information into the central processor, which then plans an optimal route based on the road layout and traffic conditions.
Inside the vehicle, sensors such as angle sensors, motor speed sensors, position sensors, and pressure sensors gather critical driving data. This information is converted into electrical signals and sent to the central processing unit, where it is analyzed and used to control the steering, acceleration, and braking systems, forming a closed-loop control system that ensures smooth and safe operation.
**Artificial Intelligence in Driverless Vehicles (Drive.Ai)**
1. **Environmental Awareness**
This is a key area in computer vision research, often referred to as SLAM (Simultaneous Localization and Mapping). Laser-based SLAM systems have shown great potential in mapping and positioning, allowing the vehicle to understand its environment in real time.
2. **Object Recognition**
This includes lane detection, traffic sign recognition, pedestrian and vehicle detection, and motion tracking. Convolutional Neural Networks (CNNs) have become the leading technology in this field, serving as the foundation for decision-making in autonomous vehicles. CNNs also complement LiDAR by helping identify obstacles that might be missed by other sensors.
3. **Behavioral Decision-Making Systems**
These systems handle global navigation, local obstacle avoidance, and rule-based driving strategies. Three main technologies are used:
- **Rule-Based Logic and Inference**: Algorithms like A*, D*, and DWA are used for path planning and obstacle avoidance. FSM (Finite State Machine) is commonly used for rule-based driving.
- **Genetic Algorithms**: When multiple strategies are available, genetic algorithms help find the best solution quickly, especially when traditional methods are too slow or complex.
- **Neural Networks**: These are increasingly used to train autonomous systems to mimic human driving behavior. However, neural networks are often considered "black boxes," making their decisions hard to interpret. This lack of transparency raises concerns about safety and reliability.
4. **Vehicle Control Systems**
Beyond traditional PID control, modern systems incorporate neural networks and fuzzy logic for more adaptive and intelligent control, improving responsiveness and efficiency.
**Common Driving Decision Strategies in Autonomous Systems**
1. **Traditional Strategy: A* + CNN + DWA**
This approach combines the A* algorithm for global path planning with convolutional neural networks for image processing and the DWA algorithm for real-time obstacle avoidance. It is widely used in systems like Google’s self-driving car.
2. **Advanced Strategy: Neural Network-Based Control**
In this method, the entire perception and control process is handled by a neural network. The raw camera input is fed directly into the network, which outputs steering, acceleration, and braking commands. This system uses millions of nodes and can make decisions without explicit programming, but it remains a black box, raising questions about trust and safety.
As AI continues to evolve, the integration of rule-based logic and neural networks will likely shape the future of autonomous driving, balancing transparency, adaptability, and safety.
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