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Cited by 386

The DOI matches its Crossref record; 168 of the 168 references it cites were found in a registry or an index.

Overview of Environment Perception for Intelligent Vehicles

Zhu, Hao, Yuen, Ka-Veng, Mihaylova, Lyudmila, Leung, Henry·2017·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2017.2658662
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Abstract

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.

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Cites

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Citations

Cites

Show all 168 references

Research works printed but not linked: 24

  1. grangier, 2009, Deep convolutional networks for scene parsing, Proc ICML Deep Learn Workshop, 1
  2. 10.1109/TITS.2004.828175 · doi:10.1109/tits.2004.828175
  3. wang, 2009, A novel lane feature extraction algorithm based on digital interpolation, Proc 17th Eur Signal Process Conf, 480
  4. 10.1109/TITS.2015.2391131 · doi:10.1109/tits.2015.2391131
  5. samadzadegan, 2006, Automatic lane detection in image sequences for vision based navigation purposes, Proc ISPRS Image Eng Vis Metrol, 1
  6. 10.1109/TAES.2010.5417152 · doi:10.1109/taes.2010.5417152
  7. 10.1155/2008/386705 · doi:10.1155/2008/386705
  8. 10.1007/s00521-007-0120-z · DOI
  9. 10.1109/IVS.2011.5940429 · doi:10.1109/ivs.2011.5940429
  10. 10.1109/TITS.2004.828169 · doi:10.1109/tits.2004.828169
  11. 10.1109/ICWAPR.2010.5576425 · doi:10.1109/icwapr.2010.5576425
  12. fleyeh, 2005, Road and traffic sign detection and recognition, 10th EWGT Meeting and 16th Mini-EURO Conference, 644
  13. chen, 2009, A maximum likelihood approach to joint registration, association and fusion for multi-sensor multi-target tracking, Proc Int Conf Inf Fusion, 686
  14. 10.1109/TAES.2004.1337456 · doi:10.1109/taes.2004.1337456
  15. 10.1016/j.ins.2015.06.042 · doi:10.1016/j.ins.2015.06.042
  16. 10.1109/ICME.2007.4284928 · doi:10.1109/icme.2007.4284928
  17. liu, 2002, Real-time recognition of road traffic sign in motion image based on genetic algorithm, Proc Int Conf Mach Learn Cybern, 83
  18. zu, 2006, Realtime obstacle detection and tracking based on constrained Delaunay triangulation, Proc Int IEEE Conf Intell Transp Syst, 548
  19. 10.1109/EuRAD.2014.6991322 · doi:10.1109/eurad.2014.6991322
  20. 10.1109/TITS.2011.2157497 · doi:10.1109/tits.2011.2157497
  21. 10.1109/TITS.2012.2188393 · doi:10.1109/tits.2012.2188393
  22. 10.1109/IVS.2009.5164330 · doi:10.1109/ivs.2009.5164330
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  24. 10.1109/IVS.2007.4290268 · doi:10.1109/ivs.2007.4290268

Cited by

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Metadata

Authors
Zhu, H., Yuen, K.-V., Mihaylova, L., & Leung, H.
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017-10
Volume
18
Issue
10
Pages
2584-2601
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2017.2658662
OpenAlex
W2588221994
Cited by
386
References
168
FWCI
23.29
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Funders
  • National Natural Science Foundation of China (61301033)
  • Wenfeng Talents of Chongqing University of Posts and Telecommunications
  • Research Fund of young-backbone university teacher in Chongqing province
  • Innovation Team Project of Chongqing Education Committee (CXTDX201601019)
  • Research Funds of Chongqing University of Posts and Telecommunications (A2012-25, A2012-81)
  • Mobility Grant with China, multi-vehicle tracking and classification for intelligent transportation systems from the U.K. Royal Society fund (E150823)

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CoChat. (2026). Citation check: Overview of Environment Perception for Intelligent Vehicles. https://app.cochat.ai/papers/10.1109/tits.2017.2658662
The paper
Zhu, H., Yuen, K.-V., Mihaylova, L., & Leung, H. (2017). Overview of Environment Perception for Intelligent Vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2017.2658662

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.