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	-	-

Aim Of The Project:

Identifying the direction of the vehicle based on the camera input.

Objective of the Project:

• Development of MATLAB Simulink model as per requirement.
• Tag the requirements to the simulink model; tagging requirement 1 & requirement 2 to their corresponding subsystems is fine.
• MBD compliant changes, Data Dictionary creation & code generation is added advantage, and that is not scope of this project.

Introduction:

Vehicle examination is a critical undertaking in different applications. It is an important process in several purposes. Various such applications are driver assistance system, electronic toll collection, intelligent parking system. Particularly in wrongdoing anticipation and mischance examination, the assignment can give helpful data.
One such application in order to enhance the detection of vehicles is to determine the direction of vehicles. Using the increasing need for security consciousness.

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.

Development of the Target Model:

As per the requirement as shown below the target model has been developed:

1. 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).

2. 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).

The Overall Model of Vehicle Direction Detection is shown below:

The Subsystem of the main model is shown below:

As per Requirement 1, the corresponding subsystem is shown below:

As per Requirement 2, the corresponding subsystem is shown below:

Procedures:

1. Tagging of the Requirement with the Model:

The requirement is written in MS-Word which is then tagged with each subsystem as shown below:

2. Linking the Model with SLDD:

After finishing the model design, the signals and the calibration parameters used in the Model are linked in the SLDD as shown below:

3. Resolving and Propagating the Signal at various stages:

All the input and output signals are resolved at their origin and they are propagated at the corresponding stages as shown below:

4. Changes in Configuration parameters for simulation and code generation:

For Simulation the Solver Setting choosen are as follows:

For Code Generation, the embedded coder is as target file as shown below:

Results and Discussion:

Simulation Analysis:

After Simulation of the Model, the following results are coming in the Display block which is connected to check whether the model is working as per requirement or not:

Here the output of the display block is denoting that the Right Turn is executed which is because the Initial Value of the input signal SteeringWheel_YawDegreeInput is taken as 30 in the SLDD as shown below:

Similarly, it is done for Left Turn and Straight Drive as shown below:

Left Turn:

Straight Drive:

Code Generation:

After selecting the target model as Embedded Coder, the building and generation of the Code of the respective model has been done, the .C file of the Vehicle_Direction_Detection model is shown below:

/*
 * File: Vehicle_Direction_Detection.c
 *
 * Code generated for Simulink model 'Vehicle_Direction_Detection'.
 *
 * Model version                  : 1.12
 * Simulink Coder version         : 9.3 (R2020a) 18-Nov-2019
 * C/C++ source code generated on : Wed Aug  4 11:48:09 2021
 *
 * Target selection: ert.tlc
 * Embedded hardware selection: Intel->x86-64 (Windows64)
 * Code generation objectives: Unspecified
 * Validation result: Not run
 */

#include "Vehicle_Direction_Detection.h"
#include "Vehicle_Direction_Detection_private.h"

/* External outputs (root outports fed by signals with default storage) */
ExtY_Vehicle_Direction_Detect_T Vehicle_Direction_Detection_Y;

/* Real-time model */
RT_MODEL_Vehicle_Direction_De_T Vehicle_Direction_Detection_M_;
RT_MODEL_Vehicle_Direction_De_T *const Vehicle_Direction_Detection_M =
  &Vehicle_Direction_Detection_M_;

/* Model step function */
void Vehicle_Direction_Detection_step(void)
{
  /* local block i/o variables */
  int16_T Vehicle_Turn_Status;

  /* Outputs for Atomic SubSystem: '<S1>/Requirement1' */
  /* Switch: '<S2>/Switch' incorporates:
   *  Constant: '<S2>/Constant'
   *  Constant: '<S2>/Constant2'
   *  Inport: '<Root>/SteeringWheel_YawDegreeInput'
   *  Switch: '<S2>/Switch1'
   */
  if (SteeringWheel_YawDegreeInput >= 30) {
    Vehicle_Turn_Status = Right_Turn_AngularLimit;
  } else if (SteeringWheel_YawDegreeInput != 0) {
    /* Switch: '<S2>/Switch1' incorporates:
     *  Constant: '<S2>/Constant1'
     */
    Vehicle_Turn_Status = Left_Turn_AngularLimit;
  } else {
    Vehicle_Turn_Status = Straight_Drive_Steering_Angle;
  }

  /* End of Switch: '<S2>/Switch' */
  /* End of Outputs for SubSystem: '<S1>/Requirement1' */

  /* Outputs for Atomic SubSystem: '<S1>/Requirement2' */
  /* Outport: '<Root>/Vehicle_Direction_Indicator' incorporates:
   *  Constant: '<S3>/Constant'
   *  Constant: '<S3>/Constant1'
   *  Constant: '<S3>/Constant2'
   *  Inport: '<Root>/CameraInput_RoadSign'
   *  Logic: '<S3>/Logical Operator'
   *  Logic: '<S3>/Logical Operator1'
   *  Logic: '<S3>/Logical Operator2'
   *  RelationalOperator: '<S3>/Relational Operator'
   *  RelationalOperator: '<S3>/Relational Operator1'
   *  RelationalOperator: '<S3>/Relational Operator2'
   */
  Vehicle_Direction_Detection_Y.Vehicle_Direction_Indicator[0] =
    ((Vehicle_Turn_Status == RightTurn_RoadSign) || CameraInput_RoadSign);
  Vehicle_Direction_Detection_Y.Vehicle_Direction_Indicator[1] =
    ((Vehicle_Turn_Status == LeftTurn_RoadSign) || CameraInput_RoadSign);
  Vehicle_Direction_Detection_Y.Vehicle_Direction_Indicator[2] =
    ((Vehicle_Turn_Status == Straight_RoadSign) || CameraInput_RoadSign);

  /* End of Outputs for SubSystem: '<S1>/Requirement2' */
}

/* Model initialize function */
void Vehicle_Direction_Detection_initialize(void)
{
  /* (no initialization code required) */
}

/* Model terminate function */
void Vehicle_Direction_Detection_terminate(void)
{
  /* (no terminate code required) */
}

/*
 * File trailer for generated code.
 *
 * [EOF]
 */

Model Advisor Report:

The model advisor report following the MAAB, JMAAB and MISRA C guidelines are shown below:

Conclusion:

The model corresponding to Vehicel Direction Detection is successfully implemented. All the compliant changes pertaining to MBD, requirement tagging, linking of the model to the SLDD, Code generation and ultimately generation of the Model Advisor Report is presented with detailed analysis.