Project 2 Adaptive Cruise Control

Main Project – Adaptive Cruise Control (ACC)

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.
  
  

Objective of Main Project:

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

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	-	-

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.

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.

1)PROCEDURE:

As per the Requirement document, implemented the Adaptive Cruise Control feature.
Lead vehicle
• To detect the lead vehicle speed, simply added both the radar & camera inputs & the corresponding output is read as Speed profile output.

2)DEVELOPMENT OF MODEL:-

As per the Requirement document, implemented the Adaptive Cruise Control feature.

1)Lead Vehicle

2)Drive Vehicle

3)Adaptive Cruise Control Algorithm

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.

According to the given logic, the subsystem is created and added both the signals and is given as the subsystem output as LeadVehicle_Speed. The input signal labels of the subsystem are propagated and output signal labels are resolve to Simulink signal object.But it is not exactly as the original system. The actual system collects the data from the camera and radar and according to the data the algorithm works out and detects the lead vehicle speed.

By improvising and assumption the logic has been implemented. The camera produces certain value as input for vehicle detection. Similar to that the radar
gives the certain value as output if the vehicle is detected. If the vehicle is detected in camera and not in radar the speed has to be decreased. If the vehicle is detected in camera and radar then vehicle to be go for braking mode.

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.

According to the given logic, the input signals are added to get the drive vehicle speed. But according to the MAB guidelines the only two signals must be added hence two add block is used. Then the radar input is the Lead vehicle detected. But we cannot give two names to the signal according to MAB guidelines. Hence Signal Conversion block is used.But it is not exactly as the original system. The actual system collects the data from the camera and radar and according to the data the algorithm works out and detects the drive vehicle speed.
By improvising and assumption the logic has been implemented. The camera produces certain value as input for vehicle detection. Similar to that the radar gives the certain value as output if the vehicle is detected. If the vehicle is detected in camera and not in radar the speed has to be decreased. If the vehicle is detected in camera and radar then vehicle to be go for braking mode. And according to the acceleration mode given as feedback from the ACC algorithm the speed of the vehicle is controlled.

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.

• The inport blocks should be placed at extreme left and outport blocks are placed at the extreme right.
• The zoom of the page must be 100%.
• The data type must be given in the data dictionary and must not be given directly in the blocks.
• The proper naming of the signal and blocks should be given.
• In the top level of the model, there should be only inports, one subsystem and outports. This is the rule of MBD.
• In the output signals, the code generation names are given as modelname_private.h and modelname_decl.c for header and function.
• After completing all these process, the model should be run for error checking.
• If there is no error, the code is generated by selecting Embedded coder in Apps and select Build.

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<set_speed) &&="" (leadvehicle_speed<drivevehicle_speed)="" ||="" (time_gap="=0.75*Set_Gap)]</em"></set_speed)>

3)Requirement Tagging & Traceability:-

4)Developing SLDD File:

5)Model Update:

The model update is done by Ctrl-D. this is done to check for any static errors. If no errors are coming the model is working fine without any errors.

6)Confiruration parameter Update setting:

7)Model Advisor Report:

8)Code Generation:

Summary:
The Adaptive cruise control is designed according to the requirements. The code is generated by using embedded coder. The model is checked for
MAB test using Model Advisor and report is added. The requirement blocks are linked to selection in the Word document. There are many advancements in the
Adaptive cruise control like turn by turn ACC, the vehicle automatically changes the speed according to the road condition, traffic, turning and slope. It has some
limitations, it is not advisable to use ACC in the Bad weather conditions such as heavy rain, storm, foggy and snowy road. These weather condition may affect
the sensor output which may lead to serious damage to vehicle and driver.