we are designing a standalone control system for autonomous vehicle that involves execution and coordination among different complex tasks such as Obstacle avoidance , Path finding, Person identification and optimal energy usage. The vehicle will define its own path and detect the obastacle us
AMAIL-self aware autonomous vehicle
we are designing a standalone control system for autonomous vehicle that involves execution and coordination among different complex tasks such as Obstacle avoidance , Path finding, Person identification and optimal energy usage. The vehicle will define its own path and detect the obastacle using camera interface by tensor flow algorithms. The object can be of any type it will be detected and the vehicle will find its path by its own sense that we have given open path choice to vehicle this project involves deep learning and machine learning algorithms with integrated hardware using SOC and then SOC is integrated with movemnt of the hardware such as tires of the vehicle.
As we discuss and explained about the working of our project’s hardware in our purposed work. In which we discuss that our vehicle will define its own path and will detect the obstacle on its path and find the best route for its movement. If the persons are on its way, it will detect them and if cars are present there etc. it will detect the things and find its path.
The equipment installed on our project is
For the movement and direction of path the algorithms are defined for the object detection the tensor flow algorithm is used which is integrated on the camera interface and that interface is integrated on the STM board the board then control the movement of the vehicle by sending signals through the camera interface. An IP address is defined so that we can see the movement of the car our self on the portable mobile phone or on the pc.
•Do not require human supervision
•Can predict best path for delivery.
•Live obstacle detection and avoidance efficiently.
•Self awareness of surroundings, paths and battery consumption.
•Android application for robotic operation and information.
•Energy efficient and technology efficient.
•Saves time and money.
The project is divided into three parts the main part of the project is the image processing part where the algorithms are defined and run to detect the every possible object then the deep learning algorithms to define the range and process the paths which the vehicle will identify itself that which path is more suitable and which path it should take to avoide the obstacle and reach its destination. The other part of the project is the integration of all the software part algorithms and deep learning system on the SOC so that no external thing is required to process it all the processing will be done through the Soc . The other part is the integration of whole system with motors and wheels so that it may move on its own choice and Machine learning is implemented there that vehicle will learn its destination parts how to reach there by finiding its paths itself.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| SOC STM | Equipment | 1 | 4000 | 4000 |
| Vehicle Body | Equipment | 1 | 6000 | 6000 |
| Motor driver and wheels | Equipment | 4 | 1000 | 4000 |
| Battery | Equipment | 2 | 2500 | 5000 |
| Led | Equipment | 1 | 1000 | 1000 |
| ATMEGA32 chip | Equipment | 1 | 1000 | 1000 |
| Miscellaneous | Miscellaneous | 1 | 5000 | 5000 |
| Total in (Rs) | 26000 |
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