Self- Driving With Deep Reinforcement ( Survey )

  • Hayder Salah Abdalameer College of Computer Science and Information Technology, University of Al-Qadisiyah, Iraq
  • Ali Abid Al-Shammary College of Computer Science and Information Technology, University of Al-Qadisiyah, Iraq
Keywords: Autonomous driving, Deep Reinforcement Learning, Reinforcement learning, Time-to-Collision (TTC), conventional nural Network, Detection And Ranging ( LiDAR )

Abstract

We will focus concentrating on the scientific and technological challenges that the development of autonomous vehicles brings to a number of different manufacturers and research organizations (AVs). It is anticipated that automobiles operated by humans will continue to be a common sight on the roads for the foreseeable future;  nevertheless, it is possible that autonomous vehicles may coexist in the same traffic environments. In order to keep traffic moving smoothly throughout the various sorts of mixed-use zones, autonomous vehicles (AVs) need to have driving rules and negotiation abilities that are analogous to those used by humans. In order to develop driving abilities that are comparable to those of humans, model-free deep reinforcement learning is used to simulate the actions of a skilled human driver. In this simulation, the difficulty of avoiding static obstacles on a two-lane roadway is investigated .

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Published
2022-08-12
How to Cite
Abdalameer, H., & Al-Shammary, A. (2022). Self- Driving With Deep Reinforcement ( Survey ). Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(3), Comp Page 10-21. https://doi.org/10.29304/jqcm.2022.14.3.982
Section
Computer article