Adaptive Cruise Control

Main Project – Adaptive Cruise Control (ACC)

Aim: To develop an Adaptive Cruise Control feature in MATLAB Simulink based on the requirements.

Overview:

Adaptive Cruise Control Feature for passenger cars allows the host vehicle to adapt to the speed in line with the flow of traffic. Driving in heavy traffic or keeping a safe distance to the preceding vehicle calls for a high level of concentration. The Adaptive Cruise Control feature can reduce the stress on the driver by automatically controlling the vehicle speed & maintaining a predefined minimum distance to the preceding vehicle. As a consequence, the driver enjoys more comfort & can concentrate on the road a little better.

A radar sensor is usually at the core of the Adaptive Cruise Control. Installed at the front of the vehicle, the system permanently monitors the road ahead. As long as the road ahead is clear, the cruise control feature maintains the speed set by the driver. If the system spots a slower vehicle within its detection range, it gently reduces speed by releasing the accelerator or actively engaging the brake control system. If the vehicle ahead speeds up or changes lanes, the cruise control automatically accelerates to the driver’s desired speed.

Standard Adaptive Cruise Control can be activated from speeds of around 30 km/h (20 mph) upwards and supports the driver, primarily on cross-country journeys or on freeways. The cruise control 'stop & go' variant is also active at speeds below 30 km/h (20 mph). It can maintain the set distance to the preceding vehicle even at very low speeds and can decelerate to a complete standstill. When the vehicle remains stopped longer, the driver needs only to reactivate the system, for example by briefly stepping on the gas pedal to return to cruise control mode. In this way, cruise control 'stop & go' supports the driver even in heavy traffic and traffic jams.

Since Adaptive Cruise Control is a comfort and convenience system, brake interventions and vehicle acceleration only take place within defined limits. Even with Adaptive Cruise Control switched on, it remains the driver’s responsibility to monitor the speed and distance from the vehicle in front.

Objectives:

1. Developing Adaptive Cruise Control feature as per the Requirement Document using MATLAB Simulink.
2. Follow all the MBD related processes: Requirement Tagging & Traceability, SLDD creation, Configuration Parameter changes, Model Advisor check & Code Generation.
3. In Configuration Parameters: enable “Support Floating Numbers” under Code Generation settings.
4. Use Embedded Coder to generate the code.
5. If choosing code generation, Storage class for Input signals: ImportedExtern; Storage class for Output signal: Export to File; Storage class for local signals: localizable; Storage class for calibration signals: Const.
6. Choose sample time for all signals as 0.01s

Simulink Model:

The below picture shows the subsystem Adaptive_Cruise_Control which has 9 inputs (CameraInput_LeadVehicle, RadarInput_LeadVehicle, CameraInput_DriveVehicle, CameraInput_DriveVehicle, Time_Gap, Set_Speed, Set_Gap, CruiseSwitch, SetSwitch) and one output (Acceleration_Mode) as per the requirements. All the inputs and output signals were resolved.

Here, the below picture shows two subsystems, Lead_Vehicle and Drive_Vehicle and a chart named Adaptive_Cruise_Control_Algorithm with the necessary inputs as mentioned in the requirements.

The Simulink model was developed based on the requirements given; the subsystems, charts and their respective logic are as follows:

Requirement 1– Lead Vehicle:

• A Lead Vehicle is a vehicle that is driving on the road ahead of our drive vehicle. Two input signals (Signal Name: CameraInput_LeadVehicle & RadarInput_LeadVehicle).
• For complexity reasons, Sensor fusion algorithms are excluded.
• Add both the radar & camera inputs & the corresponding output is read as Speed profile output (Signal Name: LeadVehicle_Speed).
• Speed data of the lead vehicle is critical in implementing the Adaptive Cruise Control algorithm.

Requirement 2 – Drive Vehicle:

• Drive Vehicle is the vehicle driven by the user & this is the vehicle that has ACC algorithm in it.
• Like the Lead Vehicle, the Drive Vehicle algorithm also has 2 input signals (Signal Name: CameraInput_DriveVehicle, RadarInput_DriveVehicle) & one signal coming as an Input to this subsystem (Signal Name: Acceleration_Mode) – three inputs into this requirement in total.
• Like the above requirement, sensor fusion techniques will also be deployed here, for complexity reasons we are ignoring them.
• Two output signals come from this subsystem (Signal Name: DriveVehicle_Speed & LeadVehicle_Detected).
• Signal DriveVehicle_Speed is the summation of three input signals mentioned above & LeadVehicle_Detected is renamed from Input Signal RadarInput_DriveVehicle by mere use of Signal Conversion block.

Requirement 3c – ACC ON MODE state logic:

This state will be activated when the input signal SetSwitch is equal to 1. There are 6 sub-states to this state logic: They are:

• LeadVehicle_Detected_Follow (Default)
• LeadVehicle_Not_Detected
• LeadVehicle_Detected_Resume
• LeadVehicle_Not_Detected_Resume
• LeadVehicle_Speed_lessthan_Set_Speed
• LeadVehicle_Speed_equal_Set_Speed

The below picture shows the logic behind the Adaptive_Cruise_Control_Algorithm chart.

Requirement 3c (i) – LeadVehicle_Detected_Follow (ACC ON MODE):

• This is the default substate inside the ACC ON MODE state. Output signal Acceleration_Mode is equal to 2.
• Condition to transit from LeadVehicle_Detected_Follow to LeadVehicle_Not_Detected; Input signal condition LeadVehicle_Detected == 0.
• Condition to transit from LeadVehicle_Detected_Follow to LeadVehicle_Speed_lessthan_Set_Speed; Input Signals condition (LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed) || (Time_Gap < Set_Gap).

Requirement 3c (ii) – LeadVehicle_Not_Detected (ACC ON MODE):

• Output signal Acceleration_Mode is equal to 1.
• Condition to transit from LeadVehicle_Not_Detected to LeadVehicle_Detected_Follow; Input signals condition [(LeadVehicle_Detected==1) && (DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)]
• Condition to transit from LeadVehicle_Not_Detected to LeadVehicle_Speed_lessthan_Set_Speed; Input signals condition [(LeadVehicle_Detected == 1) && (LeadVehicle_Speed < Set_Speed) || (Time_Gap < Set_Gap)]

Requirement 3c (iii) – LeadVehicle_Detected_Resume (ACC ON MODE):

• Output signal Acceleration_Mode is equal to 3.
• Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Detected_Follow; Input signals condition [(DriveVehicle_Speed == Set_Speed) && (LeadVehicle_Speed >= Set_Speed) && (Time_Gap >= Set_Gap)]
• Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Not_Detected_Resume; Input signal condition LeadVehicle_Detected==0.
• Condition to transit from LeadVehicle_Detected_Resume to LeadVehicle_Speed_equal_Set_Speed; Input Signal condition [(DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed > DriveVehicle_Speed) && (Time_Gap >= Set_Gap)]

Requirement 3c (iv) - LeadVehicle_Not_Detected_Resume (ACC ON MODE):

• Output signal Acceleration_Mode is equal to 1.

Requirement 3c (v) - LeadVehicle_Speed_lessthan_Set_Speed (ACC ON MODE):

• Output signal Acceleration_Mode is equal to 4.
• Condition to transit from LeadVehicle_Speed_lessthan_Set_Speed to LeadVehicle_Not_Detected; Input signal condition [(LeadVehicle_Detected == 0) && (DriveVehicle_Speed == Set_Speed)]
• Condition to transit from LeadVehicle_Speed_lessthan_Set_Speed to LeadVehicle_Speed_equal_Set_Speed; Input signals condition [((LeadVehicle_Speed*1.25>=DriveVehicle_Speed) && (LeadVehicle_Speed * 0.75<=DriveVehicle_Speed)) && (DriveVehicle_Speed < Set_Speed) && ((Time_Gap<=1.25*Set_Gap) && (Time_Gap >=0.75*Set_Gap))]

Requirement 3c (vi) - LeadVehicle_Speed_equal_Set_Speed (ACC ON MODE):

• Output signal Acceleration_Mode is equal to 5.
• Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Not_Detected_Resume; Input signal conditions is [(LeadVehicle_Detected == 0) || (DriveVehicle_Speed <= Set_Speed)]
• Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Detected_Resume; Input signal conditions [(DriveVehicle_Speed < Set_Speed) && (LeadVehicle_Speed > DriveVehicle_Speed) || (Time_Gap >= Set_Gap)]
• Condition to transit from LeadVehicle_Speed_equal_Set_Speed to LeadVehicle_Speed_lessthan_Set_Speed; Input signals conditions [(LeadVehicle_Speed<Set_Speed)&& LeadVehicle_Speed<DriveVehicle_Speed) || (Time_Gap == 0.75*Set_Gap)]

Signals & Calibration Data List:

Below is a table containing all the input and output signals data with their Signal Type, Data Type, Dimensions, Min, Max, Initial Values and Units.

Signal / Calibration Name	Signal Type	Data Type	Dimension	Min	Max	Initial Value	Units
CameraInput_LeadVehicle	Input	Uint8	1	0	255	-	-
RadarInput_LeadVehicle	Input	Uint8	1	0	255	-	-
CameraInput_DriveVehicle	Input	Uint8	1	0	255	-	-
RadarInput_DriveVehicle	Input	Uint8	1	0	255	-	-
Time_Gap	Input	Uint8	1	0	255	-	-
Set_Speed	Input	Uint8	1	0	255	-	-
Set_Gap	Input	Uint8	1	0	255	-	-
CruiseSwitch	Input	Boolean	1	0	1	-	-
SetSwitch	Input	Boolean	1	0	1	-	-
Acceleration_Mode	Output	Uint8	1	0	255	-	-
LeadVehicle_Speed	Output	Uint8	1	0	255	-	-
DriveVehicle_Speed	Output	Uint8	1	0	255	-	-
LeadVehicle_Detected	Output	Uint8	1	0	255	-	-

SLDD:

Based on the above Signals & Calibration Data List, an SLDD file named Adaptive_Cruise_Control_DD.sldd was created with all the necessary input and output parameters.

For the chart Adaptive_Cruise_Control_Algorithm, all the input and output signals were declared as shown below:

Model Advisor:

Upon setting the Configuration Parameters, the next step taken was to generate Model Advisor Report. This was carried by selecting the Model Advisor option. The below pictures shows the Model Advisor dialogue along with the report with the summary of the tests carried by Task.

Code Generation:

The next step is to generate C code for the model using Embedded C Code app. The below pictures shows the C - code generated along with the code generation report and necessary .h and .c files.

Conclusions:

• The Adaptive Cruise Control feature was developed using the MATLAB Simulink environment as per the Requirement Document.
• Created SLDD file as per the Signals and Calibration Data List.
• Model Advisor Report is generated.
• Code Generation steps are carried and the report is generated.