Project 2 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 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 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.

The above picture gives us an idea of how Adaptive Cruise Control (ACC) works in vehicles.

As usual, we are asked to implement this ACC feature using simulink and generate code.

The main objectives of this project include:

1. Developing Adaptive Cruise Control feature as per the Requirement Document using MATLAB & Simulink.
2. Following 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.

Let us create the model as per our requirement document.

We have requirements of three types:

1. Lead Vehicle - This is to find out if there are vehicles or objects in front of our vehicle (Drive Vehicle). If there are vehicles in front of us, calculate the speed of those vehicles.

2. Drive Vehicle - The lead vehicle data is processed and given as input to our vehicle. Our vehicle will calculate the required speed depending upon the lead vehicle speed and drive within those parameters.

3. ACC algorithm - This is a process or workflow to be followed while we are enabling our Adaptive Cruise Control feature in the vehicle. 

All these requirements are to be modeled as sub-systems in simulink. The inports, outports and the signals should also be named properly as per the requirement.  

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.

The requirements are fuilfilled using the inport & outport blocks and the Add block. It is now created as a sub-system. Name the subsystem as Requirement 1.

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.

 

The requirement is modeled exactly. We have also used the blocks (Add block, Signal conversion block) and signal names(CameraInput_DriveVehicle, RadarInput_DriveVehicle & Acceleration_Mode) suggested in the requirement.

The inport and outport blocks are used to create the input & output ports for the subsystem. 

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.

We are asked to model this ACC algorithm as a stateflow model. Use the Chart block form simulink to create a stateflow process. The inport & outport blocks and the chart block is used to create this requirement. 

The different state machines, state actions & the transition conditions are also given in the requirement. We will look at them to create a complete algorithm within the chart block.

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.
• LeadVehicle_Speed_equal_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 < Set_Speed) && (LeadVehicle_Speed < DriveVehicle_Speed) || (Time_Gap==0.75*Set_Gap)].

After going through this requirement, the algorithm is implemented inside the chart block. The screenshot is attached below.

Check for undefined symbols and ports in the stateflow process after modeling the algorithm.

Now we have modeled all the requirements into sub-systems. The final output is seen below.

Here the output of the ACC algorithm (Signal name : Acceleration_Mode) is fed back as input to requirement 2 (Drive Vehicle). The output is fedback using a delay block, which delays the signal for a duration and sends it back as input. The delay block is used to avoid algebraic loop errors in simulink. They occur when the input is directly fed through by the output of the same block.

Now all these requirements must be contained in a single subsystem. This will help in generating the code for the entire model in a simpler manner.

Now the process related to MBD must be followed. 

SLDD creation:

We have named the signals. Now we must create a data dictionary and define all those signals. Use the model properties under modeling settings to create and link the data dictionary to the model.

Create the SLDD according to the calibration signals list given. Define the parameters of the signals for code generation purposes.

Requirement tagging and traceability:

Link our subsystems with the requirement documents. This will help us check whether we have satisfied the requirements.

Convert the requirement document as a word document and use the requirements option by right-clicking on the sub-system.

Configuration parameters:

Go to model settings in the main menu. Double click on the model settings button.

Now we will set the configuration parameters for the code generation. First convert our solver into a fixed-step auto solver.

Next, we have to set our target file as embedder coder. Use the target file extension ert.tlc under code generation parameters.

Go to the interface option under code generation settings. Click on Support Floating Numbers check box.

Now our configuration parameters are set. We can proceed to the code generation.

Our model advisor is run for checks throughout the model. We have enabled the MAB and J-MAAB guidelines check in the model advisor.

The model was found to be fine. Run the model for any errors before proceeding to code-generation.

Code generation :

Go to the Embedded coder app under apps section. A dialog box opens.

Under Generate code option, click on generate code. Matlab begins to generate the code out of the entire sub-system.

The code was built successfully.

The code is copied and attached below.