Project 1 (Mini Project on Vehicle Direction Detection

Mini Project- Vehicle Direction detection

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.

Aim:

• 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.

• 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.
• Choose sample time for all signals as 0.01s

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.

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:

Designing the Matlab Model:

This the main model subsystem that having two inputs SteeringWheel YawDegreelnput and Cameralnput_RoadSign entering into the Vehicle Detection Determination subsystem and one output Vehicle Direction_Indicator.

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).

Simulink Data Dictionary:

Model Advisor Report:

By running the model advisor, we can see the model error.

Code Generation:

Click on the build icon as shown in below:

Conclusion:

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.