Simulating Self Driving Car Using DCNN

A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive

Project Title

Simulating Self Driving Car Using DCNN

Project Area of Specialization

Artificial Intelligence

Project Summary

A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes image and video recognition, along with recommender systems and natural language processing (NLP).

In this project, we will use what we've learned about deep neural networks and convolutional neural networks to clone driving behavior. We will train, validate and test a model using Keras. The model will output a steering angle to an autonomous vehicle.

We have provided a simulator where we can steer a car around a track for data collection. We'll use image data and steering angles to train a neural network and then use this model to drive the car autonomously around the track.

The goal of this project will be to design and analyze algorithms to control an autonomous vehicle. This vehicle will exist in a software simulation only. The focus of this project will be on the "brain" of the car itself, not any impressive graphics or anything. Conveniently we will work for finding an appropriate physics simulator, implementing a virtual car with many of the required sensors, and creating a simple world through which the car can navigate.

Project Objectives

The main objectives and goals for our project are as follows;

• To establish a new way of interoperating deep learning in transportation.
• To develop a system for street signs and board classification.
• To develop a system for car lane detection.
• To create a fully developed system for self-driving car in simulated environment.
• To detect obstacles in its path, both stationary and moving, and take basic action such as stopping or swerving.
• To use the simulator to collect data of good driving behavior
• To design, train and validate a model that predicts a steering angle from image data.
• To use the model to drive the vehicle autonomously around the first track in the simulator. The vehicle should remain on the road for an entire loop around the track.

Project Implementation Method

recognition and processing that is specifically designed to process pixel data. CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes image and video recognition, along with recommender systems and natural language processing (NLP).

A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Traditional neural networks are not ideal for image processing and must be fed images in reduced-resolution pieces. CNN have their “neurons” arranged more like those of the frontal lobe, the area responsible for processing visual stimuli in humans and other animals. The layers of neurons are arranged in such a way as to cover the entire visual field avoiding the piecemeal image processing problem of traditional neural networks.

A CNN uses a system much like a multilayer perceptron that has been designed for reduced processing requirements. The layers of a CNN consist of an input layer, an output layer and a hidden layer that includes multiple convolutional layers, pooling layers, fully connected layers and normalization layers. The removal of limitations and increase in efficiency for image processing results in a system that is far more effective, simpler to trains limited for image processing and natural language processing. 

Benefits of the Project

• To use the simulator to collect data on good driving behavior
• To establish a new way of interoperating deep learning in transportation.
• To take initiative with Punjab Government for autonomous vehicles.
• To reduce death rates by massive volume.

Technical Details of Final Deliverable

We will be using CNN for model training using VGG16 or VGG19 along with other deep learning algorithms. 

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

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