An Automatic Parking System is a rising popular protection characteristic in contemporary-day vehicles. The meaning of this system is to help the driver decide "parking slot fitment" and "vehicle parking" once the slit has been confirmed. Various sensors such as infrared, camera, ultrasonic, RADAR, etc., are used to observe obstacles when parking.
Parking is a real challenge in congested parking lots and for inexperienced drivers. Automatic Parking Systems are limited to premium cars across the world.
In the current situation, explore the empty parking space and get the best maneuver as the parking has become boring due to the congested parking lot. The parking skills of the expert driver are transferred to an intelligent system that can lighten the driving load and improve safety in next-generation passenger vehicles. Automatic Parking Systems comes to the aid of inexperienced drivers to avoid collisions when parking in reverse.
In automatic parking systems, radar, sonar, vision, and ultrasonic sensors are used in combination to avoid obstacles in the parking path. APAS systems are available in various models from Toyota (Advanced Guided Parking System), Lexus (Intuitive Parking Assistant), Volkswagen (Park Assist), among others. Parking assistance modules cost between $500 and a few thousand dollars. This indicates the expensive nature of the sensors and algorithms built into the system and thus makes it inaccessible to small segment car owners. This has led to a lot of research and development in the use of ultrasonic sensor technology.
In this model, working is shown using three already parked cars and an ego vehicle that needs to be parked. The car will be parked at the nearest slot, either beside cars or in between them.
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An automatic parking assistance system helps the driver decide parking slot fitment and vehicle parking once the slot is confirmed. Learn APAS with MATLAB; Developed in MATLAB R2021a
Below, I am sharing some portions of the code to make the explanation part easier. It's not a complete code, and you are advised to purchase and download the entire code.
We are defining global variables, which can be used by the helper function while running of code.
In next lines we are fixing coordinates of parked cars on the figure.
%% Rectangle Coordinates for Parked car 1
These lines are used to tune parameters to use further.
WSeta=(WSeta/180)*pi; % degree to radians
Next we prepare a figure of required dimensions as well as required parameters.
Initialize Start Scenario
In the following lines of code, we plot all the required figures, and one function is also used to plot the ego vehicle in its turning position.
patch([Parked_Car2x1 Parked_Car2x2 Parked_Car2x3 Parked_Car2x4],[Parked_Car2y1 Parked_Car2y2 Parked_Car2y3 Parked_Car2y4],[0 1 1]);
patch([Parked_Car1x1 Parked_Car1x2 Parked_Car1x3 Parked_Car1x4],[Parked_Car1y1 Parked_Car1y2 Parked_Car1y3 Parked_Car1y4],[1 1 0]);
patch([Parked_Car3x1 Parked_Car3x2 Parked_Car3x3 Parked_Car3x4],[Parked_Car3y1 Parked_Car3y2 Parked_Car3y3 Parked_Car3y4],[1 0 1]);
title("Automatic parking Assistance System");
Car Parking Scenario Simulation
Now we implement the final steps of the model where the whole simulation takes place. Using two other functions car can turn and then move back to reach the respective slot. At last, it displays the parked position of the vehicle.
The average time of users parking their vehicles will be effectively reduced in this system. This intelligent parking system provides better performance, low cost, and efficient large-scale parking systems.
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