Intelligence of Autonomous Vehicles: A Concise Revisit. (23rd April 2022)
- Record Type:
- Journal Article
- Title:
- Intelligence of Autonomous Vehicles: A Concise Revisit. (23rd April 2022)
- Main Title:
- Intelligence of Autonomous Vehicles: A Concise Revisit
- Authors:
- Naz, Neelma
Ehsan, Muhammad Khurram
Amirzada, Muhammad Rizwan
Ali, Md Yeakub
Qureshi, Muhammad Aasim - Other Names:
- Khan Waliullah Academic Editor.
- Abstract:
- Abstract : Artificial intelligence- (AI-) empowered machines are devised to mimic human actions. In the automotive industry, AI plays a significant role in the development of vehicular technology. AI joins hands with the field of mechatronics to assist in the accurate execution of the vehicle functionalities. Autonomous vehicles get the scene information by using onboard sensors such as laser, radar, lidar, Global Positioning System (GPS), and vehicular communication networks. The data obtained is then used for various path planning and control techniques to make the vehicles capable of autonomously driving in complex environments. Autonomous vehicles use very up-to-date AI algorithms to localize themselves in known and unknown environments. AI algorithms are also exploited for perception, path planning, and motion control. A concise review of the state-of-the-art techniques to improve the performance of autonomous vehicles is presented.
- Is Part Of:
- Journal of sensors. Volume 2022(2022)
- Journal:
- Journal of sensors
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-04-23
- Subjects:
- Detectors -- Periodicals
681.205 - Journal URLs:
- https://www.hindawi.com/journals/js/ ↗
- DOI:
- 10.1155/2022/2690164 ↗
- Languages:
- English
- ISSNs:
- 1687-725X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 21609.xml