Adaptive Cruise Control Simulation Example

Adaptive Cruise Control (ACC) is an intelligent driving assistance system widely used in modern vehicles, capable of automatically adjusting ego-vehicle speed based on preceding vehicle velocity while maintaining a safe following distance. We can implement a simple ACC simulation example using MATLAB code.

The system's fundamental principle involves obtaining preceding vehicle distance and speed information through onboard sensors (such as radar or cameras), then calculating desired acceleration based on preset safe-distance strategies, and finally achieving speed regulation through vehicle dynamic models. A typical control architecture consists of upper-level decision algorithms and lower-level execution controllers.

In MATLAB implementation, we can construct several key modules: First, establishing a vehicle longitudinal dynamics model, which can be represented using simplified first-order or second-order systems; Second, designing distance-maintenance algorithms, commonly employing PID control methods; Finally, setting up scenario simulations, including typical conditions like preceding vehicle speed variations and cut-in/cut-out maneuvers.

During simulation, different preceding vehicle motion trajectories can be configured to validate control performance - for example, testing whether the ego-vehicle can smoothly decelerate to a safe speed when the preceding vehicle suddenly brakes, or whether it can promptly accelerate when the preceding vehicle speeds up. Additional practical factors like sensor noise and communication delays can be incorporated to make simulations more realistic.

This MATLAB implementation not only helps understand ACC working principles but also serves as an effective verification method before developing actual systems. By adjusting control parameters, developers can observe how different configurations affect following performance, which is highly beneficial for algorithm optimization. The simulation typically involves implementing state-space models for vehicle dynamics and designing feedback controllers with gain scheduling techniques.