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Summary
The decision-making and control systems of self-driving cars are the core parts of autonomous driving. This paper discusses in detail the basic principles, main methods, challenges and prospects of decision-making and control in self-driving systems. By analyzing key links such as path planning, behavior decision-making, and motion control, this paper aims to provide a theoretical basis and practical guidance for the development of self-driving technology.

1 Introduction
With the rapid development of intelligent transportation and autonomous driving technologies, the decision-making and control systems of autonomous vehicles have become particularly important. The decision-making system is responsible for the strategic and tactical decisions of the vehicle, while the control system executes these decisions to ensure that the vehicle drives along the predetermined path and method. This article will systematically introduce the design principles, key technologies and application cases of autonomous driving decision-making and control systems.

2. Autonomous driving decision system

2.1 Decision-making level
The autonomous driving decision-making system is usually divided into three levels:
- **Strategic layer**: includes route planning and navigation, which determines the overall path from the starting point to the destination.
- **Tactical layer**: involves local path planning and decision-making, such as overtaking, lane changing and obstacle avoidance.
- **Operation layer**: handles specific driving actions such as acceleration, braking and steering.

2.2 Path Planning

# 2.2.1 Global path planning
Global path planning is responsible for the overall route design from the starting point to the end point. Commonly used methods include:
- **Dijkstra algorithm**: The shortest path algorithm based on graph theory.
- **A* algorithm**: An improved Dijkstra algorithm that introduces a heuristic function to improve path search efficiency.

# 2.2.2 Local path planning
Local path planning is based on the global path and is dynamically adjusted in combination with real-time environmental information. Common methods include:
- **Rapidly Expanding Random Trees (RRT)**: Suitable for path planning in dynamic environments.
- **Bezier curve**: used to generate smooth path curves.
- **Hybrid A* algorithm**: A path planning algorithm that combines grid and continuous space.

2.3 Behavioral Decision Making

# 2.3.1 State Machine
The state machine method divides the vehicle's behavior into several discrete states, such as following, overtaking, and stopping, and realizes behavioral decisions through state transition rules.

# 2.3.2 Rule-based decision making
Based on predefined rule sets, decisions are made according to the current environment and status, which is suitable for simple scenarios.

# 2.3.3 Decision-making based on game theory
Considering the interaction and competition among multiple vehicles, the optimal decision is made through the game theory model, which is suitable for complex traffic scenarios.

# 2.3.4 Reinforcement Learning
It learns the optimal decision-making strategy through interaction with the environment and is suitable for dynamic and complex driving environments.

3. Autonomous driving control system

3.1 Motion Control

# 3.1.1 Vertical Control
Longitudinal control involves the acceleration and deceleration control of the vehicle. Common methods include:
- **PID control**: Proportional-Integral-Derivative controller, simple and effective.
- **Model Predictive Control (MPC)**: Predict future behavior based on a dynamic model and optimize control inputs.

# 3.1.2 Horizontal Control
Lateral control involves the steering control of the vehicle. Common methods include:
- **Pure Pursuit**: Calculates target path points and adjusts steering angles.
- **Lateral Deviation Control**: Control based on lateral deviation and heading angle error.

3.2 Control strategy
Combine longitudinal and lateral control to achieve stable and efficient driving of the vehicle. Common control strategies include:
- **Hierarchical control**: Decompose complex control tasks into several subtasks and implement them layer by layer.
- **Coordinated control**: Comprehensively consider multiple control objectives such as safety, comfort and energy efficiency.

4. Practical applications and challenges

4.1 Practical Application
- **Urban Traffic**: Autonomous driving systems need to cope with complex urban traffic environments, including pedestrians, bicycles, and other vehicles.
- **Highway**: In highway environments, the automated driving system focuses on lane keeping, following, and overtaking.

4.2 Challenges
- **Perceived uncertainty**: Uncertainty and errors in sensor data pose challenges to decision-making and control systems.
- **Dynamic environment**: The dynamic changes in the traffic environment require the system to have real-time response and adjustment capabilities.
- **Multi-vehicle interaction**: Interaction with other vehicles requires complex behavior prediction and decision-making.

5 Conclusion
Autonomous driving decision-making and control systems are key technologies for achieving autonomous driving. Through multi-level system designs such as path planning, behavior decision-making, and motion control, autonomous vehicles can drive safely and stably in complex traffic environments. In the future, with the continuous advancement of technology and optimization of algorithms, autonomous driving decision-making and control systems will become more intelligent and reliable.