The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications
Abstract
For an autonomous vehicle to operate safely and effectively, an accurate and robust localization system is essential. While there are a variety of vehicle localization techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localization techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g., cornering and intersections) and different environmental conditions (e.g., darkness and snow). This paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localization systems with high accuracy and robustness, however, their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.
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Citations
Cites
Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges
Pathak, P. H., Feng, X., Hu, P., & Mohapatra, P. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2476474 · Cited by 1,550
Connected Vehicles: Solutions and Challenges
Lu, N., Cheng, N., Zhang, N., Shen, X., & Mark, J. W. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2327587 · Cited by 1,252
IEEE 802.15.7 visible light communication: modulation schemes and dimming support
Rajagopal, S., Roberts, R., & Lim, S.-K. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6163585 · Cited by 1,058
Papadimitratos, P., La Fortelle, A., Evenssen, K., Brignolo, R., & Cosenza, S. (2009) · IEEE Communications Magazine · doi:10.1109/mcom.2009.5307471 · Cited by 870
An overview of Internet of Vehicles
Yang, F., Wang, S., Li, J., Liu, Z., & Sun, Q. (2014) · China Communications · doi:10.1109/cc.2014.6969789 · Cited by 665
Vehicular Ad Hoc Networks: A New Challenge for Localization-Based Systems
Boukerche, A., Oliveira, H. A. B. F., Nakamura, E. F., & Loureiro, A. A. F. (2008) · Computer Communications · doi:10.1016/j.comcom.2007.12.004 · Cited by 617
In-Car Positioning and Navigation Technologies—A Survey
Skog, I., & Handel, P. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.2011712 · Cited by 582
Robust vehicle localization in urban environments using probabilistic maps
Levinson, J., & Thrun, S. (2010) · 2010 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2010.5509700 · Cited by 581
Engoulou, R. G., Bellaïche, M., Pierre, S., & Quintero, A. (2014) · Computer Communications · doi:10.1016/j.comcom.2014.02.020 · Cited by 505
Map-Based Precision Vehicle Localization in Urban Environments
Levinson, J., Montemerlo, M., & Thrun, S. (2007) · Robotics: Science and Systems III · doi:10.15607/rss.2007.iii.016 · Cited by 477
Cited by
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“to [106] for a broader localization overview.”
BackgroundDeep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Muhammad, K., Ullah, A., Lloret, J., Ser, J. D., & de Albuquerque, V. H. C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3032227 · Cited by 659
Perception and sensing for autonomous vehicles under adverse weather conditions: A survey
Zhang, Y., Carballo, A., Yang, H., & Takeda, K. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.021 · Cited by 592
Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges
Yaqoob, I., Khan, L. U., Kazmi, S. M. A., Imran, M., Guizani, N., & Hong, C. S. (2020) · IEEE Network · doi:10.1109/mnet.2019.1900120 · Cited by 299
An Empirical Study on System Level Aspects of Internet of Things (IoT)
Swamy, S. N., & Kota, S. R. (2020) · IEEE Access · doi:10.1109/access.2020.3029847 · Cited by 267
Metadata
- Authors
- Kuutti, S., Fallah, S., Katsaros, K., Dianati, M., Mccullough, F., & Mouzakitis, A.
- Venue
- IEEE Internet of Things Journal
- Published
- 2018-04
- Volume
- 5
- Issue
- 2
- Pages
- 829-846
- ISSN
- 2327-4662 (ISSN-L)
- OpenAlex
- W2791639158
- Cited by
- 801
- References
- 41
- FWCI
- 61.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kuutti, S., Fallah, S., Katsaros, K., Dianati, M., Mccullough, F., & Mouzakitis, A. (2018). A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2018.2812300
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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