Project 1 (Mini Project on Vehicle Direction Detection

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.

Objective of Mini Project:

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_Signal 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	-	-

1. DEVELOPMENT OF MATLAB MODEL

This is the top level system. two inputs SteeringWheel_YawDegreeInput and CameraInput_RoadSign are entering into the subsystem and Vehicle_Direction_Indicator is the output.

the 2 requirements are implemented in the 2 subsystems

Requirement1:

SteeringWheel_YawDegreeInput is compared with the Right_Turn_AngularLimit, Left_Turn_AngularLimit, Straight_Drive_Steering_Angle using switch blocks and Vehicle_Turn_Status is the output. this output is a local output. this output indicates whether the vehicle is taking a right turn, left turn or a straight drive.

Requirement2:

Signal Vehicle_Turn_Signal 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, Vehicle_Direction_Indicator.

2. Requirement Tagging

all the requirements are tagged in order for better understandability. anyone who is reviewing the model or who is trying to update the model can get a clear idea of what logic is used in the system.

from the requirement manager, we can see what are all are tagged and the description

3. SLDD Creation

all the inputs, outputs, calibration parameters are saved in sldd. so while simulation the model takes all the values and its parameters from the Simulink Data Dictionary.

4. Signal Resolution for inputs and outputs

the inputs and output signals are resolved. so the signals will take the values from sldd and pass it to the logical blocks. Otherwise it may take some junk values if it is unresolved.

5. Model Update.

Model update is done by Cntrl+D. this is done to check for any static errors. If there are no errors coming, the model is working fine without any errors.

6. Simulation

The model is simulated too for chechong for errors and warnings.

7. Configuration Parameter Update

Various configuration parameters are updated as per the code generation,

the solver type should be fixed for code generation

only some configuration parameters are shown here.

8. Model Advisory Report

By running the model advisor tool, we can see commonly made modeling errors.

the Model advisor report

9.Code Generation

Build the model to generate the code

Result

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.