Project 2 Adaptive Cruise Control
Project :
Adaptive cruise control :
Aim:
- To develop the Adaptive Cruise Control feature as per the Requirement Document using MATLAB Simulink.
- To follow all the MBD related processes such as Requirement Tagging & Traceability, SLDD creation, Configuration Parameter changes, Model Advisor check & Code Generation.
Adaptive cruise control:
The main reason behind cruise control technology is to make driver relax behind the wheel. Because of this, their
driving experience becomes safer, thrilling and also saves the fuel consumption. By monitoring other vehicles and objects on
the road, adaptive cruise control enables a safe and comfortable driving experience. It helps 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. Together with information about speed limits, road curvature, accidents data and more, these choices
influence the automatically selected speed. As less expensive sensors reached the market, adaptive cruise control is steadily
becoming a standard feature in new vehicles today.
In slow-moving traffic, it automatically stops or starts vehicle movement under driver supervision. The car will
brake and accelerate on its own, while maintaining a safe distance from the vehicle in front. Intelligent cruise control can
automatically adjust the set speed to the newly detected speed limit when the driver is passing a speed limit sign. This is done
by fusing camera observation and map data to provide reliable speed restriction information.
Radar-based systems work by placing radar-based sensors on or around plastic fascia’s to detect your vehicle's
surroundings. Each radar sensor works together to create a comprehensive picture of the vehicle's proximity to other cars or
potentially hazardous objects. This type of sensor can look different depending on the design and model of the car.
ACC that uses sensory data to predict the actions of neighbouring vehicles. This means that your car might slow
down to brake for another vehicle suddenly switching lanes and, in doing so, promotes passenger safety.
Adaptive Cruise Control: Adaptive cruise control (ACC ) is a form of intelligent cruise control, also known as active cruise control, autonomous cruise control, smart cruise control, and radar cruise control. A radar sensor is installed on the car’s front grille, which can sense how close or far away the vehicle in front is. By sensing this distance, the car will “adapt” its cruise control speed to maintain a safe distance from the vehicle ahead.
Drivers can set the interval from the steering wheel at how close (or how far away) they want to be from the vehicle in front. ACC will apply the brakes in traffic relative to the set distance the driver specifies. Those who drive more defensively may want it on the farthest setting so the gap between cars in front is wider.
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.
Aim: To develop an MBD-compliant Adaptive Cruise Control feature as per the provided requirement document using MATLAB Simulink
Objectives:
- 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
Modeling Requirements:
Requirement 1– Lead Vehicle:
- Lead Vehicle is a vehicle that 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 cameras & 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 that has an 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 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 the 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 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
Requirement 3c (i) – LeadVehicle_Detected_Follow (ACC ON MODE):
- This is the default sub-state 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
Signals and calibration data:
|
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 |
- |
- |
Steps to model the Adaptive cruise control algorithm:
1) SLDD file creation
- Create a blank Simulink model and save it
- Create a new SLDD file and link it to the model by saving it in the same directory as of the model
- Go to model explorer > Open design data from model folders and subfolders > create signals and constants required
- Add signal and constant parameters as specified in the document (provided as signals and calibration data list)
2) Creating Simulink model of ACC algorithm
- We start modeling the algorithm by creating a subsystem named Adaptive_Cruise_Control_Mode with 9 input parameters and one output parameter which is the final vehicle acceleration response
- Three more subsystems are created for the three provided requirements namely Lead_Vehicle, Drive_Vehicle, and ACCAlgorithm
- Lead_Vehicle subsystem: The camera and radar inputs values are taken as inputs and Lead vehicle speed will be the output as specified in requirement1
- Drive vehicle subsystem: This block follows the conditions of Requirement2 and takes 3 input values for Camera input, radar input, and acceleration mode. There will be 2 outputs of drive vehicle speed and lead vehicle detection signal
- ACCAlgorithm subsystem: This subsystem is developed on the Requirement3 and is made using the concept of stateflow. It takes 8 inputs and gives the acceleration mode as the final output.
- ACCAlgorithm Stateflow diagram: The Stateflow chart is developed based on the 6 sub-requirements specified in Requirement3.
- Statlow symbol pane:
- SLDD FILE
3) Signal resolving and propagation
- Name the imports and signals by method of signal resolving and propagation.
- A signal is resolved when it appears for the first time and the signal is then propagated then onwards
- Signal to resolve: Right Click on the signal > properties > enter a valid signal name and click on the SLDD suggestion > check the resolve signal box > OK
- Signal propagation: Double click on the signal, add <>, and press enter.
- Or Click on the signal > properties > Check the "Show propagated signal" option > OK
- The resolved signal will be propagated
- Avoid signal overlapping by using the goto and from blocks
4) Requirement tagging
- Require tagging is the process of linking the model with a document or a file that explains the model requirements by just clicking on the name
- The word document with requirement details is opened.
- Select the heading and highlight it.
- Right Click on the subsystem that explains the highlighted portion > requirements > Link to selection in word
- We can now see in the requirement section that the subsystem is linked to the word document.
- If anyone clicks on the requirement tag, the word document containing its details will be opened.
5) Configuration of Solver settings and Code generation
- Go to model settings > Solver > select type: Fixed step ; solver: discrete (no continuous states) ;Fixed step size:0.01 > OK
- Go to code generation > system target file:select ert.tlc >OK
6) Model advisor check
- Model advisor checks are done to check compliance of the model with different guidelines
- Run the model and check for any errors
- If the model is error-free, we can proceed with the advisor check
- Here we are just performing the model advisor check on MAB standards
- Go to model advisor > select the whole system >OK
- Model advisor > select "By task"
- Select "modeling standards for MAB"
- Click on generate report. The file name and the location where the generated file will be stored are displayed there
- Once the report is generated, the advisor results can be seen with the total number of the pass, fail, warnings and tasks.
- The errors must be eliminated to 0 numbers
- The warnings must be reduced to maximum
- To correct a warning, select the folder for which the warning is shown > select sub folder with warning sign > The warning parameters and correction methods as shown there
- Follow the correction recommendation and click OK when done correcting
- Now the warning number can be seen reduced.
7) Code generation:
- Once the warnings are reduced to the maximum, we can generate a C code for the model created
- Go to model explorer > open requirement 2 > set the parameters
- Go to APPS tab > Embedded Coder > Select the model name >Gerate code /Build
- The code will be displayed and it's also saved in the selected directory.
Simulink blocks used:
- Subsystem
- Add block
- Signal conversion
- Chart
- Inport and outports
Result:
- The adaptive cruise control is modeled in Simulink as per the given requirements and MBD guidlines
- Performed requirement tagging on the requirement subsystem blocks
- Solver and code generators are configured
- SLDD file is created and linked to the model also the signals are resolved and propagated
- Model advisor report is generated and verified
- C-code for the model is generated and attached