Project 1 (Mini Project on Vehicle Direction Detection

Mini Project - Vehicle Direction Determination

AIM

• Develop a Vehicle Direction Determination model in MATLAB Simulink

OBJECTIVES

1. Development of MATLAB Simulink model as per requirement.
2. Tag the requirements to the Simulink model; tagging requirement 1 & requirement 2 to their corresponding subsystems is fine.
3. MBD compliant changes, Data Dictionary creation & code generation is added advantage, and that is not scope of this project.
4. 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.
5. Choose sample time for all signals as 0.01s

Requirement - 1:

• Steering wheel input as yaw rate (Signal name: SteeringWheel_YawDegreeInput) is the input for this system.
• This is compared against 3 angular values, one each for left turn, right turn & straight drive (Calibration Values: Right_Turn_AngularLimit, Left_Turn_AngularLimit, Straight_Drive_Steering_Angle) to say which specific direction the steering wheel is turning towards.
• Use switch blocks to compare & develop this requirement. Keep this requirement in a subsystem & output of this requirement is a local signal (Signal Name: Vehicle_Turn_Status).

Requirement – 2:

• Keep this requirement as a separate subsystem. Inputs to this requirement are local signal from requirement 1 (Signal Name: Vehicle_Turn_Status) & an input signal from camera (Signal Name: CameraInput_RoadSign), which confirms the occurrence of a road sign.
• Signal Vehicle_Turn_Status is compared against calibration values (Calibration Values: RightTurn_RoadSign, LeftTurn_RoadSign, Straight_RoadSign), if each of them is found equal, then each of the three corresponding output is compared against the camera input signal,
• Using a logical operator block, only one among them is finally given as output signal (Signal Name: Vehicle_Direction_Indicator).

Signals & Calibration Data List:

Signal / Calibration Name	Signal Type	Data Type	Dimension	Min	Max	Initial Value	Units
SteeringWheel_YawDegreeInput	Input	Int16	1	-180	180	-	Deg
CameraInput_RoadSign	Input	Boolean	1	0	1	-	-
Vehicle_Turn_Status	Local	Int16	1	-180	180	-	Deg
Right_Turn_AngularLimit	Calibration	Int16	[1 1]	-180	180	30	Deg
Left_Turn_AngularLimit	Calibration	Int16	[1 1]	-180	180	-120	Deg
Straight_Drive_Steering_Angle	Calibration	Int16	[1 1]	-180	180	0	Deg
RightTurn_RoadSign	Calibration	Int16	[1 1]	-180	180	30
LeftTurn_RoadSign	Calibration	Int16	[1 1]	-180	180	-120
Straight_RoadSign	Calibration	Int16	[1 1]	-180	180	0
Vehicle_Direction_Indicator	Output	Boolean	1	0	1	-	-

OVERVIEW

Vehicle Direction Determination

• Identifying the direction of the vehicle is one of the important & diverse features in Autonomous driving & Advanced Driver Assistance Features. This particular sub-feature of identifying the direction of vehicle is basically identifying the direction the vehicle is taking based on the camera input.
• Camera reads the road signs & stores into its memory with unique values for left turn, right turn & straight drive. Depending on the direction it is taking, final indication is given to the driver – as an indication if he is driving in the recommended direction or not.
• Vehicle Direction Determination can also be coupled along - side features like GPS systems to identify whether the vehicle is reaching its destination in an optimized manner. This sub feature can also be used along with Lane Detection, Highway Warning, Ramp Entry / Exit in Wrong Way Detection etc.

MODELLING

Designing MATLAB Simulink Model:

• Steering Wheel Yaw degree and signal from camera are the inputs to the system and we'll get the vehicle direction indication as the output from the system.

Vehicle Direction Indicator:

Requirment1:

• From the Input Steering Wheel Yaw degree, we'll compare this against 3 angular values, one each for left turn, right turn & straight drive. And will Determine which specific direction the steering wheel is turning towards.
• Output from this subsystem will be the Vehicle turn status.

Requirement2:

• The Vehicle turn status from requirement1 will be the Input to this requirement and also an input signal from camera, which confirms the occurrence of a road sign.
• Signal Vehicle Turn Status will compare against calibration values, if each of them is found equal, then each of the three corresponding output is compared against the camera input signal, only one among them is finally given as output signal.

Requirement Tagging:

• Requirements can be tag to requirement 1 & requirement 2 to their corresponding subsystems with word files.

Solver Configuration parameter:

• Solver type: Fixed
• Solver: discrete (no continues states)
• Sample time: 0.01

Code generation Parameters:

• System target file: ert.tlc ( Embdded Coder)

Data Dictionary creation: (Veh_Direction_Indicator_dd)

• A new Simulink data dictionary is created and updated signals & calibration Data List as per our requirements.

Code generation:

• By using Embedded coder app we can generate code and build our model.

• Successfully conducted model build and generated code. The generated codes and files can be seen in the current directory.

Creating Test Templets:

MIL (Model In Loop) Testing

• Open Block Parameters, treat as atomic unit.
• Right Click on the subsystem, Open Test Harness creation, for subsystem. make source as signal builder. on the advanced properties set Verification mode to Normal.

SIL (Software In Loop) Testing

• Right Click on the subsystem, Open Test Harness creation, for subsystem. make source as signal builder. on the advanced properties set Verification mode to SIL (Software In Loop) Testing.

Model Advisor Check:

• Model Advisor Check on Modelling Standards for MAB.

• Model Advisor Check completed with 9Warnings and 134 check Passed.

Model Advisor Report:

• Model Advisor report can be generated, we can choose the format html, PDF or word format.

• Html Report is generated and saved in directory.

CONCLUSION

• The vehicle direction determination is modelled as per the requirement, all the signals are defined in Simulink data dictionary. All input and output signals are resolved and propagated. Updated the model to check for static errors. Run the model advisor tool to see any errors. since there are no errors in Model Advisor Report. Code has been generated using Embedded Coder.