Artificial Intelligence in Driving Cars – A Review Paper
DOI:
https://doi.org/10.48165/acspublisher.tjmitm.2022.15Keywords:
Self-driving, Neural Network, Actuators, predictive modeling, preventive algorithms, smart discriminationAbstract
The fundamental plan behind the project is to develop an automatic automotive that may sense its setting and move while not human input. This paper proposes automotive automation, that is accomplished by recognizing the road, signals, obstacles, stop signs, responding and creating decisions, like ever-changing the course of the vehicle, stopping red signals, stopping signs, and moving on inexperienced signals mistreatment Neural Network. Self-driving automotive processes input, tracks a track and sends directions to the actuators that management acceleration, braking, and steering. The software tracks traffic by means that of hard-coded rules, preventive algorithms, prognosticative modeling, and “smart” discrimination on objects, serving to the computer code to follow rules on transport
References
Truong-Dong Do, Minh-Thien Duong, Quoc-Vu Dang, and My-Ha Le, “Real-Time Self-Driving Car Navigation Using Deep Neural Network” in International Conference on Green Technology and Sustainable Development (GTSD), 2018.
Miao, Xiaodong, Shunming Li, and HuanShen. “ONBOARD LANE DETECTION SYSTEM FOR INTELLIGENT
VEHICLE BASED ON MONOCULAR VISION.” International Journal on Smart Sensing & Intelligent Systems 5.4 (2012).
HajerOmrane, Mohamed Slim Masmoudi, and Mohamed Masmoudi, “Neural controller of an autonomous driving mobile robot by an embedded camera” in International Conference on Advanced Technologies For Signal and Image Processing - ATSIP, 2018.
Abdur R. Fayjie, Sabir Hossain, DoukhiOualid, and Deok Jin Lee, “Driverless Car: Autonomous Driving Using Deep Reinforcement Learning. In Urban Environment” in 15th International Conference on Ubiquitous Robots (UR) Hawaii Convention Center, Hawai’i, USA, June 27-30, 2018
Malay Shah, Prof, RupalKapdi, “Object Detection Using Deep Neural Networks”, in International Conference on Intelligent Computing and Control Systems ICICCS 2017.
Aditya Kumar Jain, “Working model of Self-driving car using Convolutional Neural Network, Raspberry Pi and Arduino”, in Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology ICECA 2018.
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012). Image Net Classification with Deep Convolutional. Advances in neural information processing systems, 1097-1105.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation
Alex Krizhevsky, IlyaSutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc., 2012.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Trinity Journal of Management, IT & Media (TJMITM)
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
