Project 1 (Mini Project on Vehicle Direction Detection

Mini Project - Vehicle Direction Determination

General Overview:

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_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	-	-

Ans:

Aim:

To create a Vehicle Direction Determination model in simulink based on the requirment and create SLDD with code generation.

Theory:

In our work, a CMOS camera is mounted in the interior on the windshield of a test car and the optical axis is parallel to the road surface. The camera captures the RGB color road environment image forward. For the considerations of realization in an embedded system with limited hardware resources, the procedures of our proposed refined vehicle detection algorithm are shown in Fig. 1 and described as follows. Firstly, the region of interest (ROI) area, the region of vehicles possibly appearing in the image, is defined. The left and right boundaries of the ROI are the left and right boundaries of the captured image. The top of the ROI is the horizon line in the image. The bottom of the ROI is 5 m in front of the test car in the image, in our experience. Consequently, many unnecessary influences (e.g., lamps, traffic lights, signs, etc.) outside the ROI are excluded. Next, to reduce the computing complexity, the color image of the ROI will be transformed from RGB color space to YCbCr">YCbCr color space. Only the luminance component Y">Y of the image is required for subsequent image processes.

Simulink model:

The main subsystem Vehicle_Direction_Detection with given input as Steering wheel and the camera input.

The hide the inside logic we have used the Content preview option.

Inside the main subsytem:

There are 2 more Subsystem for the the 2 requirment each.1st subsytem input is steeringinput and the output of which is again given to the 2nd subsystem as shown in the below image.

Inside Requirement1 system logic:

• Two Switch block is used along wiht the 4 signal, 1 input signal as steering wheel_yaw Degreeinput and 3 calibration signals.
• 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).
• According to the Steering angle the direction of the vehicle can be detected.

Inside the Requiremnet subsystem 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).
• AND gate are used to compare the camera and the Calibration values.When both the camera and the vehicle turn status are true then we determine the direction of the vehicle.

Creating the SLDD:

Result:

By connecting the two subsystem we are ready with the final model.

The model takes 2 input signal 1 as the steertng and 1 from the camera,depending on the angle and the camera input the vehicle direction is detemined.

In subsystem 1 the direction of the steering is detected and in subsytem 2 the Direction is comapred against the camera signal by using the logical operator AND.

Now ,our model is simulating correctly now we need to check for the model advisor .

Check Model advisor :

Now we are ready to generate the final code by using the Embaded Coder for the model

Code Generation:

Conclusion:

Thus we have succesfully created a simulink model according to the requirement ,and determined the direction of the vehcicle.