Adaptive Cruise Control System

Simulate adaptive cruise control using model predictive controller

• Library:

• Model Predictive Control Toolbox / Automated Driving

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Description

The Adaptive Cruise Control System block simulates an adaptive cruise control (ACC) system that tracks a set velocity and maintains a safe distance from a lead vehicle by adjusting the longitudinal acceleration of an ego vehicle. The block computes optimal control actions while satisfying safe distance, velocity, and acceleration constraints using model predictive control (MPC).

To customize your controller, for example to use advanced MPC features or modify controller initial conditions, click Create ACC subsystem.

Ports

Input

Set velocity — Ego vehicle velocity setpoint

nonnegative scalar

Ego vehicle velocity setpoint in m/s. When there is no lead vehicle, the controller tracks this velocity.

Time gap — Safe time gap

nonnegative scalar

Safe time gap in seconds between the lead vehicle and the ego vehicle. This time gap is used to calculate the minimum safe following distance constraint. For more information, see Safe Following Distance.

Longitudinal velocity — Ego vehicle velocity

nonnegative scalar

Ego vehicle velocity in m/s.

Relative distance — Distance between lead vehicle and ego vehicle

positive scalar

Distance in meters between lead vehicle and ego vehicle. To calculate this signal, subtract the ego vehicle position from the lead vehicle position.

Relative velocity — Velocity difference between lead vehicle and ego vehicle

scalar

Velocity difference in meters per second between lead vehicle and ego vehicle. To calculate this signal, subtract the ego vehicle velocity from the lead vehicle velocity.

Minimum longitudinal acceleration — Minimum ego vehicle acceleration

negative scalar

Minimum ego vehicle longitudinal acceleration constraint in m/s². Use this input port when the minimum acceleration varies at run time.

Dependencies

To enable this port, select Use external source for the Minimum longitudinal acceleration parameter.

Maximum longitudinal acceleration — Maximum ego vehicle acceleration

positive scalar

Maximum ego vehicle longitudinal acceleration constraint in m/s². Use this input port when the maximum acceleration varies at run time.

Dependencies

To enable this port, select Use external source for the Maximum longitudinal acceleration parameter.

Enable optimization — Controller optimization enable signal

scalar

Controller optimization enable signal. When this signal is:

• Nonzero, the controller performs optimization calculations and generates a Longitudinal acceleration control signal.

• Zero, the controller does not perform optimization calculations. In this case, the Longitudinal acceleration output signal remains at the value it had when the optimization was disabled. The controller continues to update its internal state estimates.

Dependencies

To enable this port, select the Use external signal to enable or disable optimization parameter.

External control signal — Longitudinal acceleration applied to ego vehicle

scalar

Actual longitudinal acceleration in m/s² applied to the ego vehicle. The controller uses this signal to estimate the ego vehicle model states. Use this input port when the control signal applied to the ego vehicle does not match the optimal control signal computed by the model predictive controller. This mismatch can occur when, for example:

• The Adaptive Cruise Control System is not the active controller. Maintaining an accurate state estimate when the controller is not active prevents bumps in the control signal when the controller becomes active.

• The acceleration actuator fails and does not provide the correct control signal to the ego vehicle.

Dependencies

To enable this port, select the Use external control signal for bumpless transfer between ACC and other controllers parameter.

Output

Longitudinal acceleration — Acceleration control signal

scalar

Acceleration control signal in m/s² generated by the controller.

Parameters

Parameters Tab

Ego Vehicle Model

Linear model from longitudinal acceleration to longitudinal velocity — Ego vehicle model

tf(1,[0.5,1,0]) (default) | LTI model | linear System Identification Toolbox™ model

The linear model from the ego vehicle longitudinal acceleration to its longitudinal velocity, specified as an LTI model or a linear System Identification Toolbox model. The controller creates its internal predictive model by augmenting the ego vehicle dynamic model.

Programmatic Use

Block Parameter: EgoModel

Type: string, character vector

Default: "tf(1,[0.5,1,0])"

Initial condition for longitudinal velocity — Initial velocity of the ego vehicle model

20 (default) | nonnegative scalar

Initial velocity in m/s of the ego vehicle model, which can differ from the actual ego vehicle initial velocity.

This value is used to configure the initial conditions of the model predictive controller. For more information, see Initial Conditions.

Programmatic Use

Block Parameter: InitialEgoVelocity

Type: string, character vector

Default: "20"

Default spacing — Minimum spacing to lead vehicle

10 (default) | nonnegative scalar

Minimum spacing in meters between the lead vehicle and the ego vehicle. This value corresponds to the target relative distance between the ego and lead vehicles when the ego vehicle velocity is zero.

This value is used to calculate the:

• Minimum safe following distance. For more information, see Safe Following Distance.

• Controller initial conditions. For more information, see Initial Conditions.

Programmatic Use

Block Parameter: DefaultSpacing

Type: string, character vector

Default: "10"

Maximum velocity — Maximum longitudinal velocity

50 (default) | positive scalar

Maximum ego vehicle longitudinal velocity in m/s.

Programmatic Use

Block Parameter: MaxVelocity

Type: string, character vector

Default: "50"

Adaptive Cruise Controller Constraints

Minimum longitudinal acceleration — Minimum ego vehicle acceleration

-3 (default) | negative scalar

Minimum ego vehicle longitudinal acceleration constraint in m/s².

If the minimum acceleration varies over time, add the Minimum longitudinal acceleration input port to the block by selecting Use external source.

Programmatic Use

Block Parameter: MinAcceleration

Type: string, character vector

Default: "-3"

Maximum longitudinal acceleration — Maximum ego vehicle acceleration

2 (default) | nonnegative scalar

Maximum ego vehicle longitudinal acceleration constraint in m/s².

If the maximum acceleration varies over time, add the Maximum longitudinal acceleration input port to the block by selecting Use external source.

Programmatic Use

Block Parameter: MaxAcceleration

Type: string, character vector

Default: "2"

Model Predictive Controller Settings

Sample time — Controller sample time

0.1 (default) | positive scalar

Controller sample time in seconds.

Programmatic Use

Block Parameter: Ts

Type: string, character vector

Default: "0.1"

Prediction horizon — Controller prediction horizon

10 (default) | positive integer

Controller prediction horizon steps. The controller prediction time is the product of the sample time and the prediction horizon.

Programmatic Use

Block Parameter: PredictionHorizon

Type: string, character vector

Default: "30"

Controller behavior — Closed-loop controller performance

0.5 (default) | scalar between 0 and 1

Closed-loop controller performance. The default parameter value provides a balanced controller design. Specifying a:

• Smaller value produces a more robust controller with smoother control actions.

• Larger value produces a more aggressive controller with a faster response time.

When you modify this parameter, the change is applied to the controller immediately.

Programmatic Use

Block Parameter: ControllerBehavior

Type: string, character vector

Default: "0.5"

Block Tab

Use suboptimal solution — Apply suboptimal solution after specified number of iterations

off (default) | on

Configure the controller to apply a suboptimal solution after a specified maximum number of iterations, which guarantees the worst-case execution time for your controller.

For more information, see Suboptimal QP Solution.

Dependencies

After selecting this parameter, specify the Maximum iteration number parameter.

Programmatic Use

Block Parameter: suboptimal

Type: string, character vector

Default: "off"

Maximum iteration number — Maximum optimization iterations

10 (default) | positive integer

Maximum number of controller optimization iterations.

Dependencies

To enable this parameter, select the Use suboptimal solution parameter.

Programmatic Use

Block Parameter: maxiter

Type: string, character vector

Default: "10"

Use external signal to enable or disable optimization — Add port for enabling optimization

off (default) | on

To add the Enable optimization input port to the block, select this parameter.

Programmatic Use

Block Parameter: optmode

Type: string, character vector

Default: "off"

Use external signal for bumpless transfer between ACC and other controllers — Add external control signal input port

off (default) | on

Select this parameter to add the External control signal input port to the block.

Programmatic Use

Block Parameter: trackmode

Type: string, character vector

Default: "off"

Create ACC subsystem — Create custom controller

button

Generate a custom ACC subsystem, which you can modify for your application. The configuration data for the custom controller is exported to the MATLAB^® workspace as a structure.

You can modify the custom controller subsystem to:

• Modify default MPC settings or use advanced MPC features.

• Modify the default controller initial conditions.

• Use different application settings, such as a custom safe following distance definition.

Model Examples

Adaptive Cruise Control System Using Model Predictive Control

Design an MPC controller that tracks a set velocity and maintains a safe distance from a lead vehicle by adjusting the longitudinal acceleration of an ego vehicle.

Adaptive Cruise Control with Sensor Fusion

Design an adaptive cruise control system that detects a lead vehicle in its environment by combining data from vision and radar sensors.

Algorithms

Safe Following Distance

By default, the model predictive controller computes the safe following distance constraint; that is, the minimum relative distance between the lead and ego vehicle, as:

$$D_R=D_S+G_T*V_E$$

Here:

• D_S is the Default spacing parameter.

• G_T is the Time gap input signal.

• V_E is the Longitudinal velocity input signal.

To define a different safe following distance constraint, create a custom cruise control system by, on the Block tab, clicking Create ACC subsystem.

Initial Conditions

By default, the model predictive controller assumes the following initial conditions:

• Longitudinal velocity of both the ego vehicle and the lead vehicle equal the Initial condition for longitudinal velocity parameter value.

• Ego vehicle longitudinal acceleration is zero.

• Relative distance between the lead vehicle and ego vehicle is:

  $$D_R=D_S+G_T*V_E$$

  Here:

  • D_S is the Default spacing parameter.

  • G_T is the time gap and is assumed to be 1.4.

  • V_E is the Initial longitudinal velocity parameter.

If the initial conditions in your model do not match these conditions, the Longitudinal acceleration output can exhibit an initial bump at the start of the simulation.

To modify the controller initial conditions to match your simulation, create a custom cruise control system by, on the Block tab, clicking Create ACC subsystem.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

PLC Code Generation
Generate Structured Text code using Simulink® PLC Coder™.