Project 1 (Mini Project on Vehicle Direction Detection

Aim:

To Identifying the direction of the vehicle is one of the important & diverse features in Autonomous driving & Advanced Driver Assistance Features.

Objective:

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

Requirements:

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	-	-

Model:

Block Used:

• Input Blocks
• Output Blocks
• Constant Blocks
• Logical operator Blocks
• Switch Blocks
• Mux Block
• Display Block

Procedure:

• The logic within the Requirement_1 is, If the input is >=30 (because the initial price is 30) the vehicle can take the correct flip, else If the input is not-equal to zero (~=0) the vehicle left_turn, else stright_drive (if the input is adequate zero).
  
  The input yaw rate is given to the center port of the switch, and therefore the threshold price is thirty. If the input is larger than threshold price then the switch can takes actuality condition (Right_Turn_AngularLimit) else false condition. within the second switch the edge price is zero, if the edge price isn't adequate zero then the switch can takes actuality condition (Left_Turn_AngularLimit) else takes the false condition (Sitight_Drive steering_Angle). This output from the second switch is given as a false condition to the primary switch. and therefore the output for the Requirement_1 is "Vehicle_Turn_Status".

• After comparision with "Vehicle_Turn_Status" if every of them is found equal, then every of the 3 corresponding output is compared against the camera signaling, by victimisation the 'logical OR' Blocks can get actuality case and to require just one among them as output, the 'Mux' block is employed.
  
  And the final output from the Vehicle_Detection Model can indicates the vehicle_Direction indication.
  Next by adding the SLDD_file to the model and by loading the standardization values and input/output values area unit outlined.

Results:

• As the output is boolean, If we get the output as (0,0,1), Then the vehicle is moving in the straight drive.
• If the output is (1,0,0), Then the vehicle is take a Right turn.
• If the output is (1,0,1), Then the vehicle is take a Left turn.

Conclusion:

Hence we have seen the vehicle performance by Identifying the direction of the vehicle is one of the important & diverse features in Autonomous driving & Advanced Driver Assistance Features.