Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks-Reference-Cited by-同舟云学术

Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

Published:2022-12 Issue:12 Volume:23 Page:22694-22715
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Muhammad Khan¹^ORCID,Hussain Tanveer²^ORCID,Ullah Hayat³^ORCID,Ser Javier Del⁴,Rezaei Mahdi²^ORCID,Kumar Neeraj⁵^ORCID,Hijji Mohammad⁶^ORCID,Bellavista Paolo⁷,de Albuquerque Victor Hugo C.⁸^ORCID

Affiliation:

1. Department of Applied Artificial Intelligence, School of Convergence, Visual Analytics for Knowledge Laboratory (VIS2KNOW Laboratory), College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea

2. Institute for Transport Studies, University of Leeds, Leeds, U.K.

3. Department of Computer Science, Intelligent Systems, Computer Architecture, Analytics, and Security Laboratory (ISCAAS Laboratory), Kansas State University, Manhattan, KS, USA

4. TECNALIA, Basque Research & Technology Alliance (BRTA), Derio, Spain

5. Department of CSED, Thapar University, Patiala, Punjab, India

6. Faculty of Computers and Information Technology (FCIT), University of Tabuk, Tabuk, Saudi Arabia

7. DISI, University of Bologna, Bologna, Italy

8. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil

Funder

European Union’s Horizon 2020 research and innovation programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

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