Self-driving cars are being developed from one day to another. It is a creative invention in which the car is controlled by the computer. It is hard to convince people that having a self-driving car is safe as they cannot trust a machine to keep them safe. A self-driving car is purely analytical in
Self Driving Car
Self-driving cars are being developed from one day to another. It is a creative invention in which the car is controlled by the computer. It is hard to convince people that having a self-driving car is safe as they cannot trust a machine to keep them safe. A self-driving car is purely analytical in the sense that it behaves exactly like a smart computer, with no emotions or distractions, because computers are faster and smarter than our minds in taking actions. As a result, a future filled with self-driving cars may be preferable. In this project, we are making a self-driving car prototype where new hardware components and methodologies are used in a self-driving car. The system consists of a raspberry pi as the main component that runs the algorithms, cameras attached with the raspberry pi, and a variety of sensors. The Arduino is also an important component in the system because it controls the car motors and their movements. Arduino receives signals from the raspberry pi and based on them, it takes the appropriate decision for the car.
The first objective of this project is to build an electric vehicle and design its autopilot system which can do road lane detection, traffic sign detection, objects detection, and tracking. By getting all this data our car will be able to make smart decisions while driving in a real environment without any human intervention. Secondly, as we know that the world is facing the challenge of global warming, so the need for electric vehicles is more than ever before. Our self-driving car will also be electric which will also help reduce harmful emissions to protect the environment.
The main component of our system will be raspberry pi which will handle all the computational tasks. A camera sensor will be attached to the raspberry pi to get the data on the car’s surroundings. First, we apply canny edge detection for the road lane detection on the video frames that we are receiving. Road lane detection is applied through some steps, first is applying the gaussian filter to smooth the video frame and remove the noise. Second is Hough transformation for feature extraction, the main goal is to identify the lines in the digital image and draw a virtual path that is to be followed by our car. We also read data of the accelerometer and gyroscope from the MPU unit and process this data by simply passing this through the low-pass and Kalman filter to reduce the noises exerted on it and to boost the sensor data. After data preprocessing we do classification by applying a support vector machine (SVM) algorithm.
In general, our cars run on fuel which promotes carbon emission and cause damage to the environment. Studies show that 94% to 96% of car accidents are occurred due to human error and on average, an individual wastes almost 19 days in a year while commuting. So, upon completing this project we will be able to overcome these challenges, and number one would be saving those lives which we lost in accidents. Secondly, save people commute time which they can utilize in doing some productive work. Third, this will help reduce carbon emissions to save the planet.
The end product will consist of a robot car with an embedded system raspberry pi on it for the computational task. A camera sensor will also be attached to the raspberry pi which will give our car a sense of sight for it to navigate in a real environment. The Arduino UNO will be responsible for the car motors and their movements; it will receive signals from the raspberry pi and make appropriate decisions for the car based on them.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Raspberry Pi 4 Model B 2GB | Equipment | 1 | 33000 | 33000 |
| Anker PowerCore 26800 Power Bank | Equipment | 1 | 9000 | 9000 |
| L298 H Bridge | Equipment | 1 | 1000 | 1000 |
| 32 GB SD Card | Equipment | 1 | 2000 | 2000 |
| Robot Chassis | Equipment | 1 | 6000 | 6000 |
| Raspberry Pi Camera | Equipment | 1 | 7500 | 7500 |
| Arduino UNO | Equipment | 1 | 5000 | 5000 |
| Probes and Track | Equipment | 1 | 3000 | 3000 |
| Cables | Equipment | 3 | 1150 | 3450 |
| Printing and Binding | Miscellaneous | 1 | 3000 | 3000 |
| Total in (Rs) | 72950 |
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