Open AccessSimulating Self Driving Car Using Deep Learning
Author(s) -
Kalyani A. Sonwane
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36454
Subject(s) - computer science , coaching , track (disk drive) , automotive industry , artificial neural network , deep learning , artificial intelligence , process (computing) , brake , set (abstract data type) , mode (computer interface) , simulation , automotive engineering , human–computer interaction , engineering , operating system , management , economics , programming language , aerospace engineering
Self-driving cars became a trending subject with a big improvement in technologies within the last decade. The project aims to coach a neural network to drive associate degree autonomous automobile agent on Udacity’s automobile Simulator's tracks. Udacity has discharged the machine as ASCII text file computer code and enthusiasts have hosted a contest (challenge) to show an automobile the way to drive victimisation solely camera pictures and deep learning. Autonomously driving an automobile needs learning to regulate steering angle, throttle and brakes. The activity biological research technique is employed to mimic human driving behaviour within the coaching model on the track. which means a dataset is generated within the machine by a user-driven automobile in coaching mode, and therefore the deep neural network model then drives the automobile in autonomous mode. 3 architectures area unit compared regarding their performance. Though the models performed well for the track it had been trained with, the important challenge was to generalize this behaviour on a second track out there on the machine. The dataset for Track_1, that was straightforward with favourable road conditions to drive, was used because the coaching set to drive the automobile autonomously on Track_2, consisting of sharp turns, barriers, elevations, and shadows. Image process and completely different augmentation techniques were accustomed tackle this downside, that allowed extracting the maximum amount data and options within the knowledge as doable. Ultimately, the automobile was ready to run on Track_2 generalizing well. The project aims at reaching an equivalent accuracy on period of time knowledge within the future.