Challenge

Model Based Development of Adaptive Cruise Control feature using MATLAB/Simulink.

AAnshul Upreti24 Feb 2022

Aim: To develop a MBD model of Adaptive Cruise Control (ACC) feature as per the requirements using MATLAB/Simulink.

General Overview:

Adaptive Cruise Control:

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, 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 stops & 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 stops & 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.

How do ACC works?

By monitoring other vehicles and objects on the road, adaptive cruise control enables a safe and comfortable driving experience. It does so by helping the driver keep a steady vehicle speed at a given moment. The driver can set their preference regarding certain factors, such as the distance to the car in front, driving mode – for example, economical, sporty, or comfortable – and others. Together with information about speed limits, road curvature, accidents data, and more, these choices influence the automatically selected speed.

Cruise control has come a long way from its early days in its quest to assist drivers on the road. When first introduced, it was only found in luxury car models due to its high production cost. As less expensive sensors reached the market, adaptive cruise control is steadily becoming a standard feature in new vehicles today.

Difference between normal cruise control and adaptive cruise control:

The origins of normal cruise control go back to 1948 when Ralph Teetor invented the speed stat. Having greatly improved since its focus on throttle control is still central to automation today. One example is automatically pressing the acceleration pedal, which enables drivers to take their foot off the pedal for a few moments when they are on a motorway with low traffic. The need to remain vigilant remains, so they can brake whenever required.

In the late 1990s, several carmakers started introducing a new generation of cruise control: adaptive cruise control. This technology relies on front radar to address the biggest limitation traditional cruise control had: the ability to correctly appreciate the speed of the vehicle in front.

This improvement significantly expanded the continuous operation time of the cruise control function, as automation allowed to control both the acceleration and braking of a vehicle. This allowed the driver to travel for long distances with their feet off the pedal, even in moderate traffic situations on the motorway. Of course, the need for them to pay attention to the road ahead remained, as cars in front could still brake or suddenly cut in.

As drivers are getting more and more comfortable with using ACC while driving, the expectation for an even longer duration of continuous operation time for the system is rising. In turn, this puts pressure for it to be further improved. As new enhancements are made, the market is shifting to a new standard in ACC, called intelligent cruise control.

Objective of the project:

Developing Adaptive Cruise Control feature as per the Requirement Document using MATLAB Simulink.
Follow all the MBD-related processes: Requirement Tagging & Traceability, SLDD creation, Configuration Parameter changes, Model Advisor check & Code Generation.
In Configuration Parameters: enable “Support Floating Numbers” under Code Generation settings.
Use Embedded Coder to generate the code.
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.
Choose sample time for all signals as 0.01s.

Requirements:

Requirement 1– Lead Vehicle:

Lead Vehicle is a vehicle which is driving in the road ahead of our drive vehicle. Two input signals (Signal Name: CameraInput_LeadVehicle & RadarInput_LeadVehicle).
Ideally sensor fusion techniques will be deployed to process & analyze data from camera & radar. For complexity reasons, let’s not adapt to any such algorithms.
We can simply 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 which has ACC algorithm in it.
Like the Lead Vehicle, 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 summation of three input signals mentioned above & LeadVehicle_Detected is renamed from Input Signal RadarInput_DriveVehicle by mere use of Signal Conversion block.

Requirement 3 – Adaptive Cruise Control Algorithm:

Adaptive Cruise Control feature has 3 major modes of operation: OFF Mode, STANDBY Mode & ON Mode. This particular requirement has to be implemented as state machine logic in Simulink.
The input signals to this state machine system are (Signal Name: Time_Gap, Set_Speed, Set_Gap, CruiseSwitch, SetSwitch).
Also, the output signals (Signal Name: DriveVehicle_Speed & LeadVehicle_Detected) from requirement-2 is fed back as an input signal into this state machine block.
Additionally, output signal (Signal Name: LeadVehicle_Speed) from requirement-1 is given as an input signal to this state machine block as well.
Output from this subsystem is a signal (Signal Name: Acceleration_Mode) which governs the vehicular speed of the drive vehicle which automatically adjusts its speed & velocity to match the lead vehicle.

Requirement 3a – ACC OFF MODE state logic:

This is the default state inside state machine logic. Output signal Acceleration_Mode is at value 0 in this state.
This state is governed by input signal CruiseSwitch.
If CruiseSwitch is equal to 1, state ACC STANDBY mode will get activated. If CruiseSwitch is equal to 0, state ACC OFF mode will get activated, from either ACC ON mode or ACC STANDBY mode

Requirement 3b – ACC STANDBY MODE state logic:

This is the second activated state inside state machine logic. Output signal Acceleration_Mode is at value 1 in this state.
This state is governed by both input signals CruiseSwitch & SetSwitch.
If CruiseSwitch is equal to 1, state ACC STANDBY mode will get activated. If CruiseSwitch is equal to 0, state ACC OFF mode will get activated, from either ACC ON mode or ACC STANDBY mode
If SetSwitch is equal to 1, state ACC ON mode will get activated. If SetSwitch is equal to 0, state ACC STANDBY mode will get activated.

Requirement 3c – ACC ON MODE state logic:

This state will be activated when 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

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

This is the default sub state inside 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*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)]

Signals & Calibration Data List:

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	-	-

Development of MATLAB model:

The Model contains 9 inputs and 4 outputs and hence the interface of the model is represented as shown below:

Lead Vehicle model:

Drive Vehicle Model:

In Drive vehicle model there are 2 subsystems that contains the requirement 2 and requirement 3.

Requirement 2:

Requirement 3 (Adaptive Cruise Control Stateflow logic):

Simulink Data Dictionary creation:

Model Advisor Report:

In this report we need to check for MAAB guidelines, JMAAB guidelines, and MISRA guidelines.

Code Generation:

The final step will be the generation of C code of the model.

Conclusion:

The MATLAB model of the Adaptive Cruise Control feature has been developed as per the requirements.
Stateflow logic is used to develop the adaptive cruise control feature.
Simulink data dictionary is created.
Model advisor report is generated.
C code is generated for the model.

Model Based Development of Adaptive Cruise Control feature using MATLAB/Simulink.

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Ship work like thisin your own course.

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Ship work like this
in your own course.