Abstract
Self-driving cars come with both confronts and openings. Many tech gigantic companies like Google, Tesla, Apple and many more are funding billions of dollars for the implementation of a driverless car. In this modern era of automation, every human need has been driven towards things to be automated. From automated traffic control to automated home, everything comes up to the rescue of human to provide a comfortable and relaxing lifestyle. After almost automating everything now mankind has moved to automate the transportation, starting with automating the vehicles. With this the first step taken is to devise a self-driving car or a driverless car, with an aim to provide human with relaxed driving. Ever since the idea immersed, every year Google redefines the model to meet the need. In this paper, an open source simulator by Udacity, known as Self driving Car Engineer has been used for collecting the dataset and executing the neural net implemented using Python in association with packages like Keras, OpenCV etc.
Highlights
Self-driving car implementation in real time is a herculean task and requires a lot of funding and many other infrastructures
The proposed work is based on the concept of Image Processing, Data Preprocessing, Deep Neural Networks, Polynomial Regression and Behavioral Cloning
There are many challenges that are being faced in the real time implementation of driverless cars
Summary
Self-driving car implementation in real time is a herculean task and requires a lot of funding and many other infrastructures. Krasniqi and Hajrizi reported the use of IoT Technology to drive the automotive industry from connected to full autonomous vehicles [2] They proposed ideas in which the autonomous car can be aided with Internet of Things (IoT). Molnar, and St. Louis, discussed different in-vehicle and self-driving technologies for crash avoidance like Lane departure warning/mitigation, curve speed warning, forward collision warning/mitigation, blind spot warning and cross-traffic alert. Louis, discussed different in-vehicle and self-driving technologies for crash avoidance like Lane departure warning/mitigation, curve speed warning, forward collision warning/mitigation, blind spot warning and cross-traffic alert These are some of the challenges to be overcome [4]. The GPS localization data of vehicles is sent to nearby stations through wireless medium of communication This model proposes 3 types of infrastructure V2V, V2I and I2V. The simulation part is done using Udacity’s Self-Driving car engineer, an open source simulator [9]
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