Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

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dc.contributor.authorMuhammad, Khan
dc.contributor.authorUllah, Amin
dc.contributor.authorLloret, Jaime
dc.contributor.authorSer, Javier Del
dc.contributor.authorDe Albuquerque, Victor Hugo C.
dc.contributor.institutionIA
dc.date.issued2021-07
dc.descriptionPublisher Copyright: © 2000-2011 IEEE.
dc.description.abstractAdvances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.en
dc.description.sponsorshipThis work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation); The work of Javier Del Ser was supported by the Basque Government through the EMAITEK and ELKARTEK Programs, as well as by the Department of Education of this institution (Consolidated Research Group MATHMODE, IT1294-19); VHCA received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6 and #430274/2018-1). Manuscript received November 8, 2019; revised March 24, 2020 and July 21, 2020; accepted October 9, 2020. Date of publication December 7, 2020; date of current version July 12, 2021. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation); The work of Javier Del Ser was supported by the Basque Government through the EMAITEK and ELKARTEK Programs, as well as by the Department of Education of this institution (Consolidated Research Group MATHMODE, IT1294-19); VHCA received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6 and #430274/2018-1). The Associate Editor for this article was K. Kant. (Corresponding author: Khan Muhammad.) Khan Muhammad is with the Department of Software, Sejong University, Seoul 143-747, South Korea (e-mail: khan.muhammad@ieee.org).
dc.description.statusPeer reviewed
dc.format.extent21
dc.identifier.citationMuhammad , K , Ullah , A , Lloret , J , Ser , J D & De Albuquerque , V H C 2021 , ' Deep Learning for Safe Autonomous Driving : Current Challenges and Future Directions ' , IEEE Transactions on Intelligent Transportation Systems , vol. 22 , no. 7 , 9284628 , pp. 4316-4336 . https://doi.org/10.1109/TITS.2020.3032227
dc.identifier.doi10.1109/TITS.2020.3032227
dc.identifier.issn1524-9050
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85097934346&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systems
dc.relation.projectIDU.S. Department of Education, ED, IT1294-19
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDConselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, 430274/2018-1-304315/2017-6
dc.relation.projectIDMinistry of Science, ICT and Future Planning, MSIP, 2019-0-00136
dc.relation.projectIDInstitute for Information and Communications Technology Promotion, IITP
dc.relation.projectIDMinistry of Science and ICT, South Korea, MSIT
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsAutonomous driving (AD)
dc.subject.keywordsartificial intelligence
dc.subject.keywordsdecision making
dc.subject.keywordsdeep learning (DL)
dc.subject.keywordsintelligent sensors
dc.subject.keywordsvehicular safety
dc.subject.keywordsvehicular technology
dc.subject.keywordsAutomotive Engineering
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.subject.keywordsSDG 11 - Sustainable Cities and Communities
dc.titleDeep Learning for Safe Autonomous Driving: Current Challenges and Future Directionsen
dc.typejournal article
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