DRIVERLESS AUTONOMOUS TAXI SERVICE

The project aims to build a monocular vision autonomous car prototype using Raspberry Pi as a CPU. An HD camera along with a LIDAR is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of

Project Title

DRIVERLESS AUTONOMOUS TAXI SERVICE

Project Area of Specialization

Artificial Intelligence

Project Summary

The project aims to build a monocular vision autonomous car prototype using Raspberry Pi as a CPU. An HD camera along with a LIDAR is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many existing algorithms like lane detection, obstacle detection are combined together to provide the necessary control to the car.

Project Objectives

Rushing around, trying to get errands done, thinking about the things to be bought from the nearest grocery store has become part of our daily schedule. Driver error is one of the most common cause of traffic accidents, and with cell phones, in-car entertainment systems, more traffic and more complicated road systems, it isn't likely to go away. With the number of accidents increasing day by day, it has become important to take over the human errors and help the mankind. All of this could come to an end with self-driving cars which just need to know the destination and then let the passengers continue with their work. This will avoid not only accidents but also bring a self-relief for minor day to day driving activities for small items. on-autonomous vehicles have been around several years, and based on online survey we have found that ratio of accident happening due to human error is quite high and reason being • Human beings are not well-suited to travel at high speed. As speed increases, our time and distance perception degrade. • Fuel wastage caused by human error is quite high. • Due to human error, traffic congestion is found to increase.

Project Implementation Method

Our main focus was on Following Vehicle, which detects and avoids obstacles, coordinate with environment, get route and follow the route. We connected required components needed for the motor driver and then there after we connected the output to the motor driver, which would be used to output specify power to modeled car to control its speed. Once the sensors were implemented in the model, the camera which was going capture the video footage, to detect the pattern was connected to the Raspberry pie. The processing of the image was done remotely on the external system. Hence using the radio waves, we send the data from the raspberry pie to the system, process the data and then send the required output back to the raspberry pie. The system will process on determining what obstacles are detected and what it should when it detects an obstacle in the environment. The obstacle could be another vehicle or pedestrians crossing the road. The remote system can also determine what speed the modeled car should travel, what direction it should travel following the specific pattern(pathway) provided for the modeled car on the road.

Benefits of the Project

We identified the problem of non-autonomous vehicles with the proposed system which reduces the human work of operating the vehicle. Furthermore, we also notice that the given system performance is much better than an average user. Since the performance is better and always consistence, we hereby come to a conclusion that the proposed system can solve the basic human error that occurs Future work that can be added to this project may be the development of a web app. Here the user can operate when the vehicle encounters two pathways to reach common destination the user can interact though a web app. Also, the user can get suggestion of nearby places to visit also though this app. In this project, a method to make a self-driving car is presented. The different hardware components and their assembly are clearly described. A novel method to determine the uneven, marked or unmarked road edges is explained in details relying upon OpenCV. using LIDAR and camera, the collisions with obstacles is avoided

Technical Details of Final Deliverable

The technical details of the project are as following:

1. A prototype Car which consists of all the sensors, motors, camera, LIDAR and the main CPU raspberry pi.
2. A Road map pattern with several STOP, Location signs and obstacles like human, animals etc.
3. Battery Charger for Car

“VNC Viewer” Mobile App to watch what the Car sees through the cameras

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Transportation

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Quality Education

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com