click here to download the project abstract/base paper

The development and improvement of technology has enabled the use of more and more processing systems and power in now-a-days cars. “Ubiquitous computing” has given the possibility of creating smarter, faster, low-power and smaller computing systems which can be integrated in automotive engine control units which can now provide multiple active safety features like real-time traffic sign detection using cameras, obstacle detection using radar systems,moving a car using the ultrasonic sensors.The images captured by the cam sensors will be send to predict the images with the dataset present and predict the movement of the car in which direction it need to move. The usage of computation units has become more and more a need in automotive safety and in the beginning of autonomous driving. Autonomous cars sense their surroundings with cameras, ultrasonic sensors and navigational paths.It also calculates speed control of the car motion in order make adjustments to the behaviour of the vehicle. Keywords: Traffic sign detection, autonomous car,Image processing, Artificial Intelligence, simulation.
Requirements: Raspberrypi 4, cam sensor, HC-SR004, L293D

Self-driving car (autonomous , robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Self-driving Cars can detect environments using a variety of techniques such as sensors and computer vision.

The main advantage of an autonomous car: driving, often tedious and stressful,can be replaced by relaxing things (communication with friends or family, reading/viewing news on the Internet, watching a movie, etc.) or other activities from the category “time is money” (preparation of a presentation, videoconference or other things specific to busy business people). Although, safety is important too. Most of the road traffic accidents are caused by the human factor, whether based on fatigue, or failing to adapt to road conditions. Humans are subject to mistake to a much greater extent than the computer, so another great advantage awaiting the authorities from the autonomous cars will be the drastic reduction of the accidents, especially of the victims. The goal of this paper is to build a prototype of an autonomous car which can detect traffic signs using a portable smart computer as hardware and open computer
vision as software. Also, a web camera is used to detect the traffic signs, an ultrasonic distance sensor to detect obstacles, a dual H-bridge driver to control the car, and a smartphone with Android application used as real-time display. A self-driving car (sometimes called an autonomous car or driverless car) is a
vehicle that uses a combination of sensors, cameras, radar and artificial intelligence (AI) to travel between destinations without a human operator. To qualify as fully autonomous, a vehicle must be able to navigate without human intervention to a predetermined destination over roads that have not been adapted for its use. Companies developing and/or testing autonomous cars include Audi, BMW, Ford, Google, General
Motors, Tesla, Volkswagen and Volvo. Google’s test involved a fleet of selfdriving cars — including Toyota Prii and an Audi TT — navigating over 140,000 miles of California streets and highways.

Feed Forward
A feedforward neural network is a biologically inspired classification algorithm. It consist of a (possibly large) number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with all the units in the previous layer. These connections are not all equal: each connection may have a different strength or weight. The weights on these connections encode the knowledge of a network. Often the units in a neural network are also called nodes. Data enters at the inputs and passes through the network, layer by layer, until it arrives at the outputs. During normal operation, that is when it acts as a classifier, there is no feedback between layers. This is why they are called feedforward neural networks.

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