The goal of this project is to make an autonomous self-driving car, capable of guiding the car around bends, avoiding obstacles and following traffic signals and different traffic signs on the road. Machine learning model guides the car in the environment t based on the data set on which it is
Self Driving Toy Robot Car Using Deep Learning
The goal of this project is to make an autonomous self-driving car, capable of guiding the car around bends, avoiding obstacles and following traffic signals and different traffic signs on the road. Machine learning model guides the car in the environment t based on the data set on which it is trained. The model uses neural networks as the underlying learning framework.
The objectives of the system are
The deep neural network, will communicate with the car using a Raspberry Pi. The embedded software in Raspberry Pi will itself communicate with the car using an Arduino Uno. The Arduino Uno will run the car. The Raspberry Pi Camera Module connects onto the Raspberry Pi. The ultrasonic distance sensor communicates with Arduino.
The embedded software system on the Raspberry Pi has the following interfaces:
1. Camera input using a built-in library in Raspbian OS.
2. Speed and turn data output to the Arduino using RPi.GPIO library.
The embedded software system on the Arduino Uno has the following interfaces:
1. Direction and speed data input using built-in Arduino libraries.
2. PWM output to motors using built-in Arduino libraries.
Recent developments in machine learning and artificial intelligence along with the ever-increasing perfformance of modern day computers have enabled the use of these technologies in developing self-driving cars. These cars have several advantages, as described below:
The car accelerates straight until a new stimulus is introduced into the environment. When encountered with a left curve car starts to turn left until a new stimulus is introduced into the environment. When encountered with a right curve car starts to turn right until a new stimulus is introduced into the environment. When the car encounters an inactive traffic signal right in front of it on the road. The car continues to move at this speed until it has passed the traffic signal, then accelerates and continues its motion with original speed, depending on the environment and the stimulus received. When the car encounters a RED traffic signal right in front of it on the road. The car stops and remains stationary until signal reverts back to green, then resumes its motion, depending on the environment and the stimulus received. When the car encounters a YELLOW signal then the car remains stationary until signal reverts back to green, then resumes its motion, depending on the environment and the stimulus received. If a GREEN Signal is encountered, the car accelerates straight until a new stimulus is introduced in the environment. Car must follow all the traffic signs according to the law. If any object appear in front of the car then car will stop and turn where car will find path.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Raspberry pi 4 | Equipment | 1 | 16000 | 16000 |
| Chassis Car parts,motors,Arduino,H-bridge,batteries,charger,camera,etc | Equipment | 1 | 11450 | 11450 |
| wires | Equipment | 7 | 120 | 840 |
| data cable for raspberry pi | Equipment | 1 | 250 | 250 |
| servo motors | Equipment | 2 | 350 | 700 |
| Sd card | Equipment | 1 | 600 | 600 |
| Travel expenses | Miscellaneous | 2 | 1350 | 2700 |
| Lunch expenses | Miscellaneous | 3 | 1200 | 3600 |
| LCD | Equipment | 1 | 3000 | 3000 |
| keyboard | Equipment | 1 | 300 | 300 |
| mouse | Equipment | 1 | 120 | 120 |
| VGA to HDMI | Equipment | 1 | 850 | 850 |
| modem | Equipment | 1 | 1500 | 1500 |
| glue gun | Equipment | 1 | 300 | 300 |
| wire solder | Equipment | 1 | 450 | 450 |
| Documentation Printing | Miscellaneous | 1 | 440 | 440 |
| Total in (Rs) | 43100 |
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