The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.
A Collaborative V2X Data Correction Method for Road Safety
Abstract
Driving safety is one of the most important points to concern on the road. Vehicles constantly generate messages under vehicle-to-everything (V2X) assisted driving. Especially, in dense urban environments, the massive messages carrying precise data can help us to improve road safety. However, vehicles do not always provide accurate data due to a variety of reasons, such as defective vehicle sensors, or selfish. It is critical to check and analyze the data supplied by vehicles in real time and correct the possible errors to eliminate the unsafe issues. In this article, we introduce a cOllaborative vehiClE dAta correctioN method (OCEAN) based on rationality and$Q$-learning techniques to correct the error V2X data for ensuring the driving safety of vehicles on the road, which can be deployed on both vehicles and road side unit. Extensive experimental results show that OCEAN can detect error V2X data up to 80$\%$and cut down 60$\%$average error distance for most attributes in vehicle data.
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“Several other V2X services have varying latency, reliability, and bandwidth requirements.”
From Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions (2024) · cites this paper as [132]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
OpenStreetMap: User-Generated Street Maps
Haklay, M., & Weber, P. (2008) · IEEE Pervasive Computing · doi:10.1109/mprv.2008.80 · Cited by 3,208
(2012) · elib (German Aerospace Center) · Cited by 1,994
Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G
Chen, S., Hu, J., Shi, Y., Peng, Y., Fang, J., Zhao, R., & Li, Z. (2017) · IEEE Communications Standards Magazine · doi:10.1109/mcomstd.2017.1700015 · Cited by 924
Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey
Abboud, K., Omar, H. A., & Zhuang, W. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2591558 · Cited by 785
Evolutionary V2X Technologies Toward the Internet of Vehicles: Challenges and Opportunities
Zhou, H., Xu, W., Chen, J., & Wang, W. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2961937 · Cited by 662
Siegel, J. E., Erb, D. C., & Sarma, S. E. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2749459 · Cited by 460
A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV
Sakiz, F., & Şen, S. (2017) · Ad Hoc Networks · doi:10.1016/j.adhoc.2017.03.006 · Cited by 422
A Vision of C-V2X: Technologies, Field Testing, and Challenges With Chinese Development
Chen, S., Hu, J., Shi, Y., Zhao, L., & Li, W. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2974823 · Cited by 419
Self-citationContour Stella Image and Deep Learning for Signal Recognition in the Physical Layer
Lin, Y., Tu, Y., Dou, Z., Chen, L., & Mao, S. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3024610 · Cited by 416
Large-scale real-world radio signal recognition with deep learning
Tu, Y., Lin, Y., Zha, H., Ju, Z., Wang, Y., Gui, G., & Mao, S. (2021) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2021.08.016 · Cited by 361
Securing Vehicle-to-Everything (V2X) Communication Platforms
Hasan, M., Mohan, S., Shimizu, T., & Lu, H. (2020) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2020.2987430 · Cited by 275
Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges
Wang, M., Lin, Y., Tian, Q., & Si, G. (2021) · IEEE Transactions on Reliability · doi:10.1109/tr.2021.3062045 · Cited by 250
Artificial intelligence for edge service optimization in Internet of Vehicles: A survey
Xu, X., Li, H., Xu, W., Liu, Z., Yao, L., & Dai, F. (2021) · Tsinghua Science & Technology · doi:10.26599/tst.2020.9010025 · Cited by 207
Misbehaviour prediction for autonomous driving systems
Stocco, A., Weiß, M., Calzana, M., & Tonella, P. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380353 · Cited by 176
Collaborative Learning of Communication Routes in Edge-Enabled Multi-Access Vehicular Environment
Wu, C., Liu, Z., Liu, F., Yoshinaga, T., Ji, Y., & Li, J. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3002253 · Cited by 162
Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications
Chen, X., Wu, C., Liu, Z., Zhang, N., & Ji, Y. (2021) · IEEE Wireless Communications · doi:10.1109/mwc.001.2000296 · Cited by 153
Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning
Ercan, S., Ayaida, M., & Messai, N. (2022) · IEEE Access · doi:10.1109/access.2021.3136706 · Cited by 109
Intelligent Content Caching Strategy in Autonomous Driving Toward 6G
Zhao, L., Li, H., Lin, N., Lin, M., Fan, C., & Shi, J. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3114199 · Cited by 85
Self-citationPhysical layer plausibility checks for misbehavior detection in V2X networks
So, S., Petit, J., & Starobinski, D. (2019) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3317549.3323406 · Cited by 82
A Novel Prediction-Based Temporal Graph Routing Algorithm for Software-Defined Vehicular Networks
Zhao, L., Li, Z., Al‐Dubai, A., Min, G., Li, J., Hawbani, A., & Zomaya, A. Y. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3123276 · Cited by 66
Self-citationTowards Experienced Anomaly Detector Through Reinforcement Learning
Huang, C., Wu, Y., Zuo, Y., Pei, K., & Min, G. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12130 · Cited by 59
Cooperative Sybil Attack Detection for Position Based Applications in Privacy Preserved VANETs
Hao, Y. S., Tang, J., & Cheng, Y. (2011) · Globecom · doi:10.1109/glocom.2011.6134242 · Cited by 58
A Misbehavior Authority System for Sybil Attack Detection in C-ITS
Kamel, J., Haidar, F., Jemaa, I. B., Kaiser, A., Lonc, B., & Urien, P. (2019) · 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon47517.2019.8993045 · Cited by 56
A sybil attack detection approach using neighboring vehicles in VANET
Grover, J., Gaur, M. S., Laxmi, V., & Prajapati, N. K. (2011) · International Conference on Security of Information and Networks (SIN) · doi:10.1145/2070425.2070450 · Cited by 55
SPIDER: A Social Computing Inspired Predictive Routing Scheme for Softwarized Vehicular Networks
Zhao, L., Zheng, T., Lin, M., Hawbani, A., Shang, J., & Fan, C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122438 · Cited by 55
Self-citationWang, X., Zhu, Y., Han, S., Yang, L., Gu, H., & Wang, F. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3109276 · Cited by 47
Intelligent Mobile Edge Caching for Popular Contents in Vehicular Cloud Toward 6G
Liu, P., Zhang, Y., Fu, T., & Hu, J. W. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3076304 · Cited by 32
A Survey of the Inadequacies in Traffic Sign Recognition Systems for Autonomous Vehicles
Magnussen, A. F., Le, N., Hu, L., & Wong, W. E. (2020) · International Journal of Performability Engineering · doi:10.23940/ijpe.20.10.p10.15881597 · Cited by 24
Toward Achieving Fine-Grained Access Control of Data in Connected and Autonomous Vehicles
Cui, J., Chen, X., Zhang, J., Zhang, Q., & Zhong, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3041860 · Cited by 24
Zhong, H., Pan, L., Zhang, Q., & Cui, J. (2019) · IEEE Access · doi:10.1109/access.2019.2933137 · Cited by 21
Big Data Architectures for Vehicle Data Analysis
Prehofer, C., & Mehmood, S. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378397 · Cited by 21
Toward Keyword Extraction in Constrained Information Retrieval in Vehicle Social Network
Yang, Z., Yu, H., Tang, J., & Liu, H. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2906799 · Cited by 19
Data Verification and Misbehavior Detection in Vehicular Ad-hoc Networks
Ghaleb, F. A. A., Zainal, A. B., & Rassam, M. A. (2015) · Jurnal Teknologi · doi:10.11113/jt.v73.4190 · Cited by 18
Physical signal-driven fusion for V2X misbehavior detection
Nguyen, V.-L., Lin, P.-C., & Hwang, R.-H. (2019) · 2019 IEEE Vehicular Networking Conference (VNC) · doi:10.1109/vnc48660.2019.9062815 · Cited by 15
Kubernetes Virtual Warehouse Placement based on Reinforcement Learning
Li, H., Li, D., Eric, W. W., Zeng, D., & Zhao, M. (2021) · International Journal of Performability Engineering · doi:10.23940/ijpe.21.07.p2.579588 · Cited by 13
Reinforcement Learning-based Misbehaviour Detection in V2X Scenarios
Sedar, R., Kalalas, C., Vázquez-Gallego, F., & Alonso-Zárate, J. (2021) · IEEE International Mediterranean Conference on Communications and Networking (MeditCom) · doi:10.1109/meditcom49071.2021.9647514 · Cited by 12
To Tolerate or To Impute Missing Values in V2X Communications Data?
Razavi‐Far, R., Wan, D., Saif, M., & Mozafari, N. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3126749 · Cited by 12
DDoS Attack Real-Time Defense Mechanism using Deep Q-Learning Network
Feng, W., & Wu, Y. (2020) · International Journal of Performability Engineering · doi:10.23940/ijpe.20.09.p5.13621373 · Cited by 7
Flagged first, then by citations
2 of the 42 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Ahmed-Zaid, 2011, Vehicle safety communicationsApplications (VSC-A) final report: Appendix volume 3 security, Glob. Positioning Syst., Nat. Highway Traffic Saf. Admin.
- Watkins, 1992, Q-learning, 279
- Krajzewicz, 2012, Recent development and applications of sumoSimulation of urban mobility, Int. J. Adv. Syst. Meas., 3, 128
Cited by
Sorted by citations
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Liu, Z., Wan, L., Guo, J., Huang, F., Feng, X., Wang, L., & Ma, J. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3340723 · Cited by 205
Liao, H., Zhou, Z., Liu, N., Zhang, Y., Xu, G., Wang, Z., & Mumtaz, S. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3194840 · Cited by 154
Self-citationYang, J., Jia, L., Guo, Z., Shen, Y., Li, X., Mou, Z., Yu, K., & Lin, J. C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106002 · Cited by 105
Self-citationYang, J., Lin, F., Chakraborty, C., Yu, K., Guo, Z., Nguyen, A., & Rodrigues, J. J. P. C. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3237960 · Cited by 98
Self-citationOpportunistic capacity based resource allocation for 6G wireless systems with network slicing
Jie, H., Yang, F., Chakraborty, C., Guo, Z., Zhang, H., Li, Z., & Yu, K. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.10.032 · Cited by 89
Self-citation
Metadata
- Authors
- Zhao, L., Chai, H., Han, Y., Yu, K., & Mumtaz, S.
- Affiliations
- Shenyang Aerospace University (CN)
- School of Computer Science, Shenyang Aerospace University, Shenyang, China
- Waseda University (JP)
- University of Aveiro (PT)
- Venue
- IEEE Transactions on Reliability
- Published
- 2022-06
- Volume
- 71
- Issue
- 2
- Pages
- 951-962
- ISSN
- 0018-9529 (ISSN-L)
- 1558-1721
- OpenAlex
- W4225976909
- Cited by
- 110
- References
- 42
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 22 · 2023: 34 · 2022: 37
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Reliability (publishedVersion)
- Funders
- Department of Science and Technology of Liaoning Province
- Science Foundation of Liaoning Province (2020-MS-237)
- Fujian Normal University (202001)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Engineering › Automotive Engineering › IoT and GPS-based Vehicle Safety Systems
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- vehicle-to-everything communication, error detection, road safety, Q-learning, rationality, roadside units, dense urban areas
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