The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Networking and Communications in Autonomous Driving: A Survey
Abstract
The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle's perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Simultaneous Wireless Information and Power Transfer (SWIPT): Recent Advances and Future Challenges
Ponnimbaduge Perera, T. D., Jayakody, D. N. K., Sharma, S. K., Chatzinotas, S., & Li, J. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2783901 · Cited by 920
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Self-citationInformation-centric networking for connected vehicles: a survey and future perspectives
Amadeo, M., Campolo, C., & Molinaro, A. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7402268 · Cited by 264
Cited by
Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges
Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3009103 · Cited by 857
Deep 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
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620
Self-citationA Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
Ahangar, M. N., Ahmed, Q. Z., Khan, F. A., & Hafeez, M. (2021) · Sensors · doi:10.3390/s21030706 · Cited by 373
Security and privacy in 6G networks: New areas and new challenges
Wang, M., Zhu, T., Zhang, T., Zhang, J., Yu, S., & Zhou, W. (2020) · Digital Communications and Networks · doi:10.1016/j.dcan.2020.07.003 · Cited by 363
Metadata
- Authors
- Wang, J., Liu, J., & Kato, N.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2019
- Volume
- 21
- Issue
- 2
- Pages
- 1243-1274
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2906647810
- Cited by
- 536
- References
- 3
- FWCI
- 17.69
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Networking and Communications in Autonomous Driving: A Survey. https://app.cochat.ai/papers/10.1109/comst.2018.2888904
- The paper
- Wang, J., Liu, J., & Kato, N. (2019). Networking and Communications in Autonomous Driving: A Survey. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2018.2888904
Data sources
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.
