Artificial Intelligence in Driving Cars – A Review Paper

Authors

  • Aniket Trinity Institute of Professional Studies, Dwarka
  • Darshika Trinity Institute of Professional Studies, Dwarka
  • Neha Aggarwal Trinity Institute of Professional Studies, Dwarka

DOI:

https://doi.org/10.48165/acspublisher.tjmitm.2022.15

Keywords:

Self-driving, Neural Network, Actuators, predictive modeling, preventive algorithms, smart discrimination

Abstract

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.

Published

2022-07-25

How to Cite

Artificial Intelligence in Driving Cars – A Review Paper. (2022). Trinity Journal of Management, IT & Media (TJMITM), 13(Special Issue), 39–44. https://doi.org/10.48165/acspublisher.tjmitm.2022.15