Project 1 (Mini Project on Vehicle Direction Detection

AIM:- Identifying the direction of the vehicle based on the camera input.

General Overview:-

Identifying the direction of the vehicle is one of the important & diverse features of Autonomous driving & Advanced
Driver Assistance Features. This particular sub-feature of identifying the direction of the vehicle is identifying the
direction the vehicle is taking based on the camera input.
The camera reads the road signs & stores them into its memory with unique values for a left turn, right turn & straight
drive. Depending on the direction it is taking, the 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 with features like GPS systems to identify whether the vehicle

is reaching its destination optimally. This sub-feature can also be used along with Lane Detection,
Highway Warning, Ramp Entry / Exit in Wrong Way Detection, etc.

The objective of the 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 the
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 Of Project:-

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 a left turn, right turn & straight drive (Calibration Values: Right_Turn_AngularLimit, Left_Turn_AngularLimit, Straight_Drive_Steering_Angle) to say which specific the direction the steering wheel is turning towards.
• Use switch blocks to compare & develop this requirement. Keep this requirement in a subsystem & the 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 a local signal from requirement 1 (Signal Name: Vehicle_Turn_Status) & an input signal from the 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 the 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_YawDegreeInputInput 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 - -

Theory:-

The ultimate goal of ADAS feature development is to make our roads safer and better suited for fully autonomous
vehicles in the long run. Still, manufacturers and buyers shouldn’t underestimate the importance of ADAS for
meeting current automotive challenges. The most significant impact of advanced driver assistance systems is in
providing drivers with essential information and automating difficult and repetitive tasks. This increases safety for
everyone on the road.
ADAS vision systems and ADAS safety systems require lots of fused sensors to monitor the vehicle’s surroundings
and what’s going on inside the car.
The most commonly used ADAS sensors today are lidar, radar, and ultrasonic. Software for self-driving cars
featuring ultrasonic sensors usually consists of multiple sensors located in the front and rear bumpers and side-view mirrors.
They transmit short sound waves and measure the time it takes for them to travel to a target object and return to
the receiver.
An ADAS safety system can rely on ultrasonic sensor technology for low-speed and short-range applications such
as blind-spot detection, self-parking, and parking assistance.
Radar and lidar are both used by ADAS engineers for object detection, collision prevention, and interaction with
traffic management systems.
Still, there are differences between these technologies. Lidar is the best solution for real-time detection, but it’s
unpleasantly expensive for mass deployment.
Radar sensors, especially long-range ones, are reliable enough and cheaper but lack precision when detecting
small objects.

Simulink Model:-

Requirment_1:-

Requirment_2:-

CODE GENERATION:-

For a generation of code, I went to the apps tab and after that selected the embedded coder, and finally, build the C
Code out of it.

/*
 * Trial License - for use to evaluate programs for possible purchase as
 * an end-user only.
 *
 * File: Vehicle_Direction_Detection.c
 *
 * Code generated for Simulink model 'Vehicle_Direction_Detection'.
 *
 * Model version                  : 1.5
 * Simulink Coder version         : 9.5 (R2021a) 14-Nov-2020
 * C/C++ source code generated on : Thu Jun 23 19:18:25 2022
 *
 * 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"

/* Real-time model */
static 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: '<Root>/Vehicle_Direction_Detection' */
  /* Switch: '<S2>/Switch1' incorporates:
   *  Constant: '<S4>/Constant'
   *  Constant: '<S5>/Constant'
   *  Inport: '<Root>/SteeringWheel_YawDegreeInput'
   *  RelationalOperator: '<S4>/Compare'
   *  RelationalOperator: '<S5>/Compare'
   *  Switch: '<S2>/Switch'
   */
  if (SteeringWheel_YawDegreeInput > 0) {
    /* Switch: '<S2>/Switch1' incorporates:
     *  Constant: '<S2>/Constant2'
     */
    Vehicle_Turn_Status = Right_Turn_AngularLimit;
  } else if (SteeringWheel_YawDegreeInput == 0) {
    /* Switch: '<S2>/Switch' incorporates:
     *  Constant: '<S2>/Constant'
     *  Switch: '<S2>/Switch1'
     */
    Vehicle_Turn_Status = Straight_Drive_Steering_Angle;
  } else {
    /* Switch: '<S2>/Switch1' incorporates:
     *  Constant: '<S2>/Constant1'
     *  Switch: '<S2>/Switch'
     */
    Vehicle_Turn_Status = Left_Turn_AngularLimit;
  }

  /* End of Switch: '<S2>/Switch1' */

  /* SignalConversion: '<S3>/Signal Conversion' incorporates:
   *  Constant: '<S3>/Constant'
   *  Constant: '<S3>/Constant1'
   *  Constant: '<S3>/Constant2'
   *  Inport: '<Root>/CameraInput_RoadSign'
   *  Logic: '<S3>/AND'
   *  Logic: '<S3>/AND1'
   *  Logic: '<S3>/AND2'
   *  RelationalOperator: '<S3>/Relational Operator'
   *  RelationalOperator: '<S3>/Relational Operator1'
   *  RelationalOperator: '<S3>/Relational Operator2'
   */
  Vehicle_Direction_Indicator[0] = ((Vehicle_Turn_Status == RightTurn_RoadSign) &&
    CameraInput_RoadSign);
  Vehicle_Direction_Indicator[1] = ((Vehicle_Turn_Status == LeftTurn_RoadSign) &&
    CameraInput_RoadSign);
  Vehicle_Direction_Indicator[2] = ((Vehicle_Turn_Status == Straight_RoadSign) &&
    CameraInput_RoadSign);

  /* End of Outputs for SubSystem: '<Root>/Vehicle_Direction_Detection' */
}

/* 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]
 */

RESULTS:-

MODEL IN LOOP:-

SOFTWARE IN LOOP:-

CONCLUSION:-

I made the model of vehicle direction determination as per the given requirements and also made SLDD for it. All input-output signals are resolved and propagated, then I check the error with the model advisor report I passed in that report,
and my code is generated successfully. In this way, I did my project and get result satisfactory.