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Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
University of California, Berkeley, 2002
C. Chen, Freeway performance measurement system (PeMS) · 2002
Earlier work this paper cites.
G. Pan, L. Sun, Z. Wu, and S. Lao, “Eyeblink-based anti-spoofing in face recognition from a generic webcamera,” in 2007 IEEE 11th International Conference on Computer Vision
2007
Earlier work this paper cites.
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “The german traffic sign recognition benchmark: a multi-class classification competition,” in The 2011 International Joint Conference on Neural Networks
2011
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems
2014
Earlier work this paper cites.
R. Timofte, K. Zimmermann, and L. Van Gool, “Multi-view traffic sign detection, recognition, and 3D localisation,” Mach. Vis. Appl
2014
Earlier work this paper cites.
T. Drutarovsky and A. Fogelton, “Eye blink detection using variance of motion vectors.,” in ECCV Workshops (3)
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18
2015
Earlier work this paper cites.
D. Zang, Z. Chai, J. Zhang, D. Zhang, and J. Cheng, “Vehicle license plate recognition using visual attention model and deep learning,” Journal of Electronic Imaging
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
GAIA, “Didi chuxing gaia initiative,” Didi Chuxing
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The Cityscapes Dataset for Semantic Urban Scene Understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
State Farm, “State farm distracted driver detection,” Kaggle
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on Intelligent Vehicles
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
P. Voigt and A. Von dem Bussche, “The EU General Data Protection Regulation (GDPR),” A Practical Guide, 1st Ed., Cham: Springer International Publishing
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Dorri, M. Steger, S. S. Kanhere, and R. Jurdak, “BlockChain: A Distributed Solution to Automotive Security and Privacy,” IEEE Communications Magazine
2017
Earlier work this paper cites.
D. Fisher-Hickey, “1.6 million UK traffic accidents,” Kaggle
2017
Earlier work this paper cites.
S. Moosavi, B. Omidvar-Tehrani, and R. Ramnath, “Trajectory annotation by discovering driving patterns,” in Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3D classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2017
Earlier work this paper cites.
J. Zhang, Y. Zheng, and D. Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction,” in Proceedings of the AAAI conference on artificial intelligence
2017
Earlier work this paper cites.
Springer, 2017
D. B. Rawat and C. Bajracharya, Vehicular cyber physical systems · 2017
Earlier work this paper cites.
Z. Zhou, F. Xiong, C. Xu, Y. He, and S. Mumtaz, “Energy-efficient vehicular heterogeneous networks for green cities,” IEEE Transactions on industrial Informatics
2017
Earlier work this paper cites.
Z. Wang, G. Wu, P. Hao, and M. J. Barth, “Cluster-wise cooperative eco-approach and departure application for connected and automated vehicles along signalized arterials,” IEEE Transactions on Intelligent Vehicles
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Nedić, A. Olshevsky, and M. G. Rabbat, “Network topology and communication-computation tradeoffs in decentralized optimization,” Proceedings of the IEEE
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang, “Bisenet: Bilateral segmentation network for real-time semantic segmentation,” in Proceedings of the European conference on computer vision (ECCV)
2018
Earlier work this paper cites.
MIT press, 2018
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction · 2018
Earlier work this paper cites.
U. DOT, “Next generation simulation (NGSIM) vehicle trajectories and supporting data,” US Department of Transportation
2018
Earlier work this paper cites.
R. W. Van Der Heijden, T. Lukaseder, and F. Kargl, “Veremi: A dataset for comparable evaluation of misbehavior detection in vanets,” in Security and Privacy in Communication Networks: 14th International Conference, SecureComm 2018, Singapore, Singapore, August 8-10, 2018, Proceedings, Part I
2018
Earlier work this paper cites.
W. Puarungroj and N. Boonsirisumpun, “Thai license plate recognition based on deep learning,” Procedia Computer Science
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Wang, C.-Y. Chan, and A. de La Fortelle, “A reinforcement learning based approach for automated lane change maneuvers,” in 2018 IEEE Intelligent Vehicles Symposium (IV)
2018
Earlier work this paper cites.
Z. Wang, G. Wu, and M. J. Barth, “A review on cooperative adaptive cruise control (cacc) systems: Architectures, controls, and applications,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
2018
Earlier work this paper cites.
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Federated learning for ultra-reliable low-latency v2v communications,” in 2018 IEEE Global Communications Conference (GLOBECOM)
2018
Earlier work this paper cites.
J. Liu, H. Xu, L. Wang, Y. Xu, C. Qian, J. Huang, and H. Huang, “Adaptive Asynchronous Federated Learning in Resource-Constrained Edge Computing,” IEEE Transactions on Mobile Computing
2018
Earlier work this paper cites.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST)
2019
Earlier work this paper cites.
S. Lu, Y. Yao, and W. Shi, “Collaborative Learning on the Edges: A Case Study on Connected Vehicles,” USENIX Workshop on Hot Topics in Edge Computing (HotEdge)
2019
Earlier work this paper cites.
I. Bilik, O. Longman, S. Villeval, and J. Tabrikian, “The rise of radar for autonomous vehicles: Signal processing solutions and future research directions,” IEEE signal processing Magazine
2019
Earlier work this paper cites.
D. Roy, T. Ishizaka, C. K. Mohan, and A. Fukuda, “Vehicle trajectory prediction at intersections using interaction based generative adversarial networks,” in 2019 IEEE Intelligent transportation systems conference (ITSC)
2019
Earlier work this paper cites.
G. Rathee, A. Sharma, R. Iqbal, M. Aloqaily, N. Jaglan, and R. Kumar, “A blockchain framework for securing connected and autonomous vehicles,” Sensors
2019
Earlier work this paper cites.
R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, et al
2019
Earlier work this paper cites.
M. Martin, A. Roitberg, M. Haurilet, M. Horne, S. Reiß, M. Voit, and R. Stiefelhagen, “Drive&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2019
Earlier work this paper cites.
C. Wang, L. Ma, R. Li, T. S. Durrani, and H. Zhang, “Exploring trajectory prediction through machine learning methods,” IEEE Access
2019
Earlier work this paper cites.
Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE transactions on neural networks and learning systems
2019
Earlier work this paper cites.
A. Folkers, M. Rick, and C. Büskens, “Controlling an autonomous vehicle with deep reinforcement learning,” in 2019 IEEE Intelligent Vehicles Symposium (IV)
2019
Earlier work this paper cites.
D. Li, D. Zhao, Q. Zhang, and Y. Chen, “Reinforcement learning and deep learning based lateral control for autonomous driving [application notes],” IEEE Computational Intelligence Magazine
2019
Earlier work this paper cites.
S. Sharma, G. Tewolde, and J. Kwon, “Lateral and longitudinal motion control of autonomous vehicles using deep learning,” in 2019 IEEE International Conference on Electro Information Technology (EIT)
2019
Earlier work this paper cites.
A. Miglani and N. Kumar, “Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges,” Vehicular Communications
2019
Earlier work this paper cites.
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed federated learning for ultra-reliable low-latency vehicular communications,” IEEE Transactions on Communications
2019
Earlier work this paper cites.
T. Nishio and R. Yonetani, “Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge,” in ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
2019
Earlier work this paper cites.
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE Journal on Selected Areas in Communications
2019
Earlier work this paper cites.
Accessed: Feb. 18, 2020. [Online]
“Flood of data will get generated in autonomous cars.” · 2020
Earlier work this paper cites.
W. Zhang, Q. Lu, Q. Yu, Z. Li, Y. Liu, S. K. Lo, S. Chen, X. Xu, and L. Zhu, “Blockchain-based federated learning for device failure detection in industrial iot,” IEEE Internet of Things Journal
2020
Earlier work this paper cites.
Z. Du, C. Wu, T. Yoshinaga, K.-L. A. Yau, Y. Ji, and J. Li, “Federated learning for vehicular internet of things: Recent advances and open issues,” IEEE Open J. Comput. Soc
2020
Earlier work this paper cites.
J. C. Jiang, B. Kantarci, S. Oktug, and T. Soyata, “Federated learning in smart city sensing: Challenges and opportunities,” Sensors
2020
Earlier work this paper cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated Optimization in Heterogeneous Networks,” Proceedings of Machine Learning and Systems
2020
Earlier work this paper cites.
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the Convergence of FedAvg on Non-IID Data,” in International Conference on Learning Representations
2020
Earlier work this paper cites.
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimization,” Advances in Neural Information Processing Systems
2020
Earlier work this paper cites.
C. He, M. Annavaram, and S. Avestimehr, “Group knowledge transfer: Federated learning of large cnns at the edge,” Advances in Neural Information Processing Systems
2020
Earlier work this paper cites.
S. R. Pokhrel and J. Choi, “A Decentralized Federated Learning Approach for Connected Autonomous Vehicles,” in 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)
2020
Earlier work this paper cites.
M. Wilbur, C. Samal, J. P. Talusan, K. Yasumoto, and A. Dubey, “Time-dependent decentralized routing using federated learning,” in 2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
D. Byrd and A. Polychroniadou, “Differentially private secure multi-party computation for federated learning in financial applications,” in Proceedings of the First ACM International Conference on AI in Finance
2020
Earlier work this paper cites.
Y. Fu, F. R. Yu, C. Li, T. H. Luan, and Y. Zhang, “Vehicular Blockchain-Based Collective Learning for Connected and Autonomous Vehicles,” IEEE Wireless Communications
2020
Earlier work this paper cites.
S. R. Pokhrel and J. Choi, “Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges,” IEEE Transactions on Communications
2020
Earlier work this paper cites.
Y. Lu, X. Huang, K. Zhang, S. Maharjan, and Y. Zhang, “Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles,” IEEE Transactions on Vehicular Technology
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Y. Liu, J. James, J. Kang, D. Niyato, and S. Zhang, “Privacy-preserving traffic flow prediction: A federated learning approach,” IEEE Internet of Things Journal
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
2020
Earlier work this paper cites.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “BDD100k: A diverse driving dataset for heterogeneous multitask learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2020
Earlier work this paper cites.
E. Alberti, A. Tavera, C. Masone, and B. Caputo, “IDDA: A large-scale multi-domain dataset for autonomous driving,” IEEE Robotics and Automation Letters
2020
Earlier work this paper cites.
H. Yang, L. Liu, W. Min, X. Yang, and X. Xiong, “Driver yawning detection based on subtle facial action recognition,” IEEE Trans. Multimed
2020
Earlier work this paper cites.
U. M. Gidado, H. Chiroma, N. Aljojo, S. Abubakar, S. I. Popoola, and M. A. Al-Garadi, “A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles,” IEEE Access
2020
Earlier work this paper cites.
Y. Liu, A. Huang, Y. Luo, H. Huang, Y. Liu, Y. Chen, L. Feng, T. Chen, H. Yu, and Q. Yang, “FedVision: An online visual object detection platform powered by federated learning,” Proceedings of the AAAI Conference on Artificial Intelligence
2020
Earlier work this paper cites.
W. Wang and J. Tu, “Research on license plate recognition algorithms based on deep learning in complex environment,” IEEE Access
2020
Earlier work this paper cites.
D. Chen, L. Jiang, Y. Wang, and Z. Li, “Autonomous driving using safe reinforcement learning by incorporating a regret-based human lane-changing decision model,” in 2020 American Control Conference (ACC)
2020
Earlier work this paper cites.
Z. Wang, Y. Bian, S. E. Shladover, G. Wu, S. E. Li, and M. J. Barth, “A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles,” IEEE Intelligent Transportation Systems Magazine
2020
Cited alongside, same era.
S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,” Journal of Field Robotics
2020
Cited alongside, same era.
S. Kuutti, R. Bowden, Y. Jin, P. Barber, and S. Fallah, “A survey of deep learning applications to autonomous vehicle control,” IEEE Transactions on Intelligent Transportation Systems
2020
Cited alongside, same era.
P. Sun, N. Aljeri, and A. Boukerche, “Machine learning-based models for real-time traffic flow prediction in vehicular networks,” IEEE Network
2020
Cited alongside, same era.
Y. Liu, Y. Tian, B. Sun, Y. Wang, and F.-Y. Wang, “Parallel lidars meet the foggy weather,” IEEE Journal of Radio Frequency Identification
2022
Later among the works it cites.
N. Pandey and S. S. Ram, “Classification of automotive targets using inverse synthetic aperture radar images,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
A. Venon, Y. Dupuis, P. Vasseur, and P. Merriaux, “Millimeter wave fmcw radars for perception, recognition and localization in automotive applications: A survey,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
Y. Liu, Y. Shen, L. Fan, Y. Tian, Y. Ai, B. Tian, Z. Liu, and F.-Y. Wang, “Parallel radars: from digital twins to digital intelligence for smart radar systems,” Sensors
2022
Later among the works it cites.
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alphaXiv is searching for related work…
2020
Cited alongside, same era.
Y. Li, Z. Zhang, Z. Zhang, and Y.-C. Kao, “Secure federated learning with efficient communication in vehicle network,” Journal of Internet Technology
2020
Cited alongside, same era.
G. Pan and M.-S. Alouini, “Flying car transportation system: Advances, techniques, and challenges,” IEEE Access
2021
Cited alongside, same era.
J. Nidamanuri, C. Nibhanupudi, R. Assfalg, and H. Venkataraman, “A progressive review: Emerging technologies for adas driven solutions,” IEEE Transactions on Intelligent Vehicles
2021
Cited alongside, same era.
M. Singh and R. Dubey, “Deep learning model based co2 emissions prediction using vehicle telematics sensors data,” IEEE Transactions on Intelligent Vehicles
2021
Cited alongside, same era.
P. Kairouz, H. B. McMahan, et al
2021
Cited alongside, same era.
W. Zhang, T. Zhou, Q. Lu, X. Wang, C. Zhu, H. Sun, Z. Wang, S. K. Lo, and F.-Y. Wang, “Dynamic-fusion-based federated learning for covid-19 detection,” IEEE Internet of Things Journal
2021
Cited alongside, same era.
S. Savazzi, M. Nicoli, M. Bennis, S. Kianoush, and L. Barbieri, “Opportunities of federated learning in connected, cooperative, and automated industrial systems,” IEEE Commun. Mag
2021
Cited alongside, same era.
2022
Later among the works it cites.
Y. Tian, J. Wang, Y. Wang, C. Zhao, F. Yao, and X. Wang, “Federated vehicular transformers and their federations: Privacy-preserving computing and cooperation for autonomous driving,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,” in European conference on computer vision
2022
Later among the works it cites.
G. Li, Y. Qiu, Y. Yang, Z. Li, S. Li, W. Chu, P. Green, and S. E. Li, “Lane change strategies for autonomous vehicles: a deep reinforcement learning approach based on transformer,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
D. C. Selvaraj, S. Hegde, N. Amati, F. Deflorio, and C. F. Chiasserini, “An ml-aided reinforcement learning approach for challenging vehicle maneuvers,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
Z. Ju, H. Zhang, X. Li, X. Chen, J. Han, and M. Yang, “A survey on attack detection and resilience for connected and automated vehicles: From vehicle dynamics and control perspective,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
N. Hussain, P. Rani, H. Chouhan, and U. S. Gaur, “Cyber security and privacy of connected and automated vehicles (cavs)-based federated learning: challenges, opportunities, and open issues,” Federated Learning for IoT Applications
2022
Later among the works it cites.
B. Ghimire and D. B. Rawat, “Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things,” IEEE Internet of Things Journal
2022
Later among the works it cites.
M. Pandey et al
2022
Later among the works it cites.
Cham: Springer International Publishing, 2022
N. Hussain, P. Rani, H. Chouhan, and U. S. Gaur, Cyber Security and Privacy of Connected and Automated Vehicles (CAVs)-Based Federated Learning: Challenges, Opportunities, and Open Issues · 2022
Later among the works it cites.
2022
Later among the works it cites.
Y. He, K. Huang, G. Zhang, F. R. Yu, J. Chen, and J. Li, “Bift: A Blockchain-Based Federated Learning System for Connected and Autonomous Vehicles,” IEEE Internet of Things Journal
2022
Later among the works it cites.
A. R. Javed, M. A. Hassan, F. Shahzad, W. Ahmed, S. Singh, T. Baker, and T. R. Gadekallu, “Integration of blockchain technology and federated learning in vehicular (iot) networks: A comprehensive survey,” Sensors
2022
Later among the works it cites.
Z. Zhu, X. Wang, Y. Zhao, S. Qiu, Z. Liu, B. Chen, and F.-Y. Wang, “Crowdsensing intelligence by decentralized autonomous vehicles organizations and operations,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
2022
Later among the works it cites.
A. M. Elbir, S. Coleri, A. K. Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas, “A hybrid architecture for federated and centralized learning,” IEEE Trans. Cogn. Commun. Netw
2022
Later among the works it cites.
M. Han, K. Xu, S. Ma, A. Li, and H. Jiang, “Federated learning-based trajectory prediction model with privacy preserving for intelligent vehicle,” International Journal of Intelligent Systems
2022
Later among the works it cites.
S. S. Sepasgozar and S. Pierre, “Fed-ntp: A federated learning algorithm for network traffic prediction in vanet,” IEEE Access
2022
Later among the works it cites.
Z. Yang, X. Zhang, D. Wu, R. Wang, P. Zhang, and Y. Wu, “Efficient asynchronous federated learning research in the internet of vehicles,” IEEE Internet Things J
2022
Later among the works it cites.
X. Zhou, R. Ke, Z. Cui, Q. Liu, and W. Qian, “Stfl: Spatio-temporal federated learning for vehicle trajectory prediction,” in 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)
2022
Later among the works it cites.
M. Naphade, S. Wang, D. C. Anastasiu, Z. Tang, M. Chang, Y. Yao, L. Zheng, M. S. Rahman, A. Venkatachalapathy, A. Sharma, Q. Feng, V. Ablavsky, S. Sclaroff, P. Chakraborty, A. Li, S. Li, and R. Chellappa, “The 6th ai city challenge,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2022
Later among the works it cites.
R. M. Fujimoto, R. Guensler, M. P. Hunter, H. Wu, M. Palekar, J. Lee, and J. Ko, “Crawdad gatech/vehicular (v. 2006-03-15),” 2022
2022
Later among the works it cites.
Z. Hu, S. Lou, Y. Xing, X. Wang, D. Cao, and C. Lv, “Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
S. Ansari, F. Naghdy, and H. Du, “Human-machine shared driving: Challenges and future directions,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
Y. Lu, J. Liang, G. Yin, L. Xu, J. Wu, J. Feng, and F. Wang, “A shared control design for steering assistance system considering driver behaviors,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
Y. Huang, J. Du, Z. Yang, Z. Zhou, L. Zhang, and H. Chen, “A survey on trajectory-prediction methods for autonomous driving,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
C. Koetsier, J. Fiosina, J. N. Gremmel, J. P. Müller, D. M. Woisetschläger, and M. Sester, “Detection of anomalous vehicle trajectories using federated learning,” ISPRS Open Journal of Photogrammetry and Remote Sensing
2022
Later among the works it cites.
G. Rjoub, J. Bentahar, and O. A. Wahab, “Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving,” in 2022 International Wireless Communications and Mobile Computing (IWCMC)
2022
Later among the works it cites.
C. Wang, X. Chen, J. Wang, and H. Wang, “Atpfl: Automatic trajectory prediction model design under federated learning framework,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
S. Wang, Y. Hong, R. Wang, Q. Hao, Y.-C. Wu, and D. W. K. Ng, “Edge federated learning via unit-modulus over-the-air computation,” IEEE Transactions on Communications
2022
Later among the works it cites.
S. Xiao, X. Ge, Q.-L. Han, and Y. Zhang, “Resource-efficient platooning control of connected automated vehicles over vanets,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
F. Tian, Z. Li, F.-Y. Wang, and L. Li, “Parallel learning-based steering control for autonomous driving,” IEEE Transactions on Intelligent Vehicles
2022
Later among the works it cites.
R. K. Manna, D. J. Gonzalez, V. Chellapandi, M. Mar, S. S. Kannan, S. Wadekar, E. J. Dietz, C. M. Korpela, and A. El Gamal, “Control challenges for high-speed autonomous racing: Analysis and simulated experiments,” SAE International Journal of Connected and Automated Vehicles
2022
Later among the works it cites.
X. Yuan, J. Chen, J. Yang, N. Zhang, T. Yang, T. Han, and A. Taherkordi, “Fedstn: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems
2022
Later among the works it cites.
X. Yuan, J. Chen, N. Zhang, C. Zhu, Q. Ye, and X. S. Shen, “Fedtse: Low-cost federated learning for privacy-preserved traffic state estimation in iov,” in IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
2022
Later among the works it cites.
Y. Lei, S. L. Wang, C. Su, and T. F. Ng, “Oes-fed: a federated learning framework in vehicular network based on noise data filtering,” PeerJ Computer Science
2022
Later among the works it cites.
P. Zheng, Y. Zhu, Y. Hu, and A. Schmeink, “Data-driven extreme events modeling for vehicle networks by personalized federated learning,” in 2022 International Symposium on Wireless Communication Systems (ISWCS)
2022
Later among the works it cites.
X. Li, L. Lu, W. Ni, A. Jamalipour, D. Zhang, and H. Du, “Federated multi-agent deep reinforcement learning for resource allocation of vehicle-to-vehicle communications,” IEEE Transactions on Vehicular Technology
2022
Later among the works it cites.
A. Taïk, Z. Mlika, and S. Cherkaoui, “Clustered Vehicular Federated Learning: Process and Optimization,” IEEE Transactions on Intelligent Transportation Systems
2022
Later among the works it cites.
W. Lobato, J. B. Da Costa, A. M. de Souza, D. Rosário, C. Sommer, and L. A. Villas, “Flexe: Investigating federated learning in connected autonomous vehicle simulations,” in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)
2022
Later among the works it cites.
2022
Later among the works it cites.
Q. Zhang, H. Wen, Y. Liu, S. Chang, and Z. Han, “Federated-reinforcement-learning-enabled joint communication, sensing, and computing resources allocation in connected automated vehicles networks,” IEEE Internet of Things Journal
2022
Later among the works it cites.
R. Albelaihi, L. Yu, W. D. Craft, X. Sun, C. Wang, and R. Gazda, “Green Federated Learning via Energy-Aware Client Selection,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference
2022
Later among the works it cites.
W. Fang, Z. Yu, Y. Jiang, Y. Shi, C. N. Jones, and Y. Zhou, “Communication-efficient stochastic zeroth-order optimization for federated learning,” IEEE Transactions on Signal Processing
2022
Later among the works it cites.
Z. Wang, C. Lv, and F.-Y. Wang, “A new era of intelligent vehicles and intelligent transportation systems: Digital twins and parallel intelligence,” IEEE Transactions on Intelligent Vehicles
2023
Closest in time.
C. Park, G. S. Kim, S. Park, S. Jung, and J. Kim, “Multi-agent reinforcement learning for cooperative air transportation services in city-wide autonomous urban air mobility,” IEEE Transactions on Intelligent Vehicles
2023
Closest in time.
B. Chen, X. Zeng, W. Zhang, L. Fan, S. Cao, and J. Zhou, “Knowledge sharing-based multi-block federated learning for few-shot oil layer identification,” Energy
2023
Closest in time.
B. Chen, T. Chen, X. Zeng, W. Zhang, Q. Lu, Z. Hou, J. Zhou, and S. Helal, “Dfml: Dynamic federated meta-learning for rare disease prediction,” IEEE/ACM Transactions on Computational Biology and Bioinformatics
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
L. Yuan, L. Su, and Z. Wang, “Federated transfer-ordered-personalized learning for driver monitoring application,” IEEE Internet of Things Journal
2023
Closest in time.
C. Zhao, Z. Gao, Q. Wang, K. Xiao, Z. Mo, and M. J. Deen, “Fedsup: A communication-efficient federated learning fatigue driving behaviors supervision approach,” Future Gener. Comput. Syst
2023
Closest in time.
R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song, et al
2023
Closest in time.
2023
Closest in time.
C. Cui, Y. Ma, J. Lu, and Z. Wang, “REDFormer: Radar enlightens the darkness of camera perception with transformers,” IEEE Transactions on Intelligent Vehicles
2023
Closest in time.
H. Li, Z. Cai, J. Wang, J. Tang, W. Ding, C.-T. Lin, and Y. Shi, “Fedtp: Federated learning by transformer personalization,” IEEE Transactions on Neural Networks and Learning Systems
2023
Closest in time.
J. Zhang, L. Zhao, K. Yu, G. Min, A. Y. Al-Dubai, and A. Y. Zomaya, “A novel federated learning scheme for generative adversarial networks,” IEEE Transactions on Mobile Computing
2023
Closest in time.
J. Lu, L. Han, Q. Wei, X. Wang, X. Dai, and F.-Y. Wang, “Event-triggered deep reinforcement learning using parallel control: A case study in autonomous driving,” IEEE Transactions on Intelligent Vehicles
2023
Closest in time.
A. A. Korba, A. Boualouache, B. Brik, R. Rahal, Y. Ghamri-Doudane, and S. M. Senouci, “Federated learning for zero-day attack detection in 5g and beyond v2x networks,” in AlgoTel 2023-25èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications
2023
Closest in time.
W. Zhang, Z. Bao, Y. Liu, L. Xu, Q. Lu, H. Ning, X. Wang, S. Yang, F.-Y. Wang, and Z. Li, “Cfsl: A credible federated self-learning framework,” IEEE Internet of Things Journal
2023
Closest in time.
L. Yuan, Y. Ma, L. Su, and Z. Wang, “Peer-to-peer federated continual learning for naturalistic driving action recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
Closest in time.
R. Du, K. Han, R. Gupta, S. Chen, S. Labi, and Z. Wang, “Driver monitoring-based lane-change prediction: A personalized federated learning framework,” in 2023 IEEE Intelligent Vehicles Symposium (IV)
2023
Closest in time.
R. Parekh, N. Patel, R. Gupta, N. K. Jadav, S. Tanwar, A. Alharbi, A. Tolba, B.-C. Neagu, and M. S. Raboaca, “Gefl: gradient encryption-aided privacy preserved federated learning for autonomous vehicles,” IEEE Access
2023
Closest in time.
2023
Closest in time.
M. Naphade, S. Wang, D. C. Anastasiu, Z. Tang, M.-C. Chang, Y. Yao, L. Zheng, M. S. Rahman, M. S. Arya, A. Sharma, Q. Feng, V. Ablavsky, S. Sclaroff, P. Chakraborty, S. Prajapati, A. Li, S. Li, K. Kunadharaju, S. Jiang, and R. Chellappa, “The 7th ai city challenge,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
2023
Closest in time.
H. Mu, L. Yuan, and J. Li, “Human sensing via passive spectrum monitoring,” IEEE Open Journal of Instrumentation and Measurement
2023
Closest in time.
A. Ferrari, D. Micucci, M. Mobilio, and P. Napoletano, “Deep learning and model personalization in sensor-based human activity recognition,” Journal of Reliable Intelligent Environments
2023
Closest in time.
Y. Ma, L. Yuan, A. Abdelraouf, K. Han, R. Gupta, Z. Li, and Z. Wang, “M2dar: Multi-view multi-scale driver action recognition with vision transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
Closest in time.
X. Liao, X. Zhao, Z. Wang, Z. Zhao, K. Han, R. Gupta, M. J. Barth, and G. Wu, “Driver digital twin for online prediction of personalized lane change behavior,” IEEE Internet of Things Journal
2023
Closest in time.
S. Teng, X. Hu, P. Deng, B. Li, Y. Li, Y. Ai, D. Yang, L. Li, Z. Xuanyuan, F. Zhu, et al
2023
Closest in time.
R. Xie, C. Li, X. Zhou, and Z. Dong, “Asynchronous federated learning for real-time multiple licence plate recognition through semantic communication,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2023
Closest in time.
T. Liu, L. Cui, B. Pang, and Z.-P. Jiang, “A unified framework for data-driven optimal control of connected vehicles in mixed traffic,” IEEE Transactions on Intelligent Vehicles
2023
Closest in time.
2023
Closest in time.
S. Hosseinalipour, S. Wang, N. Michelusi, V. Aggarwal, C. G. Brinton, D. J. Love, and M. Chiang, “Parallel successive learning for dynamic distributed model training over heterogeneous wireless networks,” IEEE/ACM Transactions on Networking
2023
Closest in time.
2023
Closest in time.
S. Dai, S. I. Alam, R. Balakrishnan, K. Lee, S. Banerjee, and N. Himayat, “Online federated learning based object detection across autonomous vehicles in a virtual world,” in 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)
2023
Closest in time.
J. Hu, S. Sun, J. Lai, S. Wang, Z. Chen, and T. Liu, “CACC simulation platform designed for urban scenes,” IEEE Transactions on Intelligent Vehicles
2023
Closest in time.
H. Wang, J. Xie, and M. M. A. Muslam, “Fair: Towards impartial resource allocation for intelligent vehicles with automotive edge computing,” IEEE Transactions on Intelligent Vehicles
2023
Closest in time.
S. Wang, S. Hosseinalipour, V. Aggarwal, C. G. Brinton, D. J. Love, W. Su, and M. Chiang, “Towards cooperative federated learning over heterogeneous edge/fog networks,” IEEE Communications Magazine
2023
Closest in time.
2023
Closest in time.
submitted to IEEE Forum for Innovative Sustainable Transportation Systems 2024
V. P. Chellapandi, Y. Nagaraj, J. Supplee, S. Hernandez-Gonzalez, H. Borhan, and S. H. Żak, “Predictive Control of Diesel Oxidation Catalysts with Federated Learning in Connected Vehicles,” · 2024
Closest in time.
submitted to IEEE ICC 2024
W. Fang, D.-J. Han, and C. G. Brinton, “Submodel partitioning in hierarchical federated learning: Algorithm design and convergence analysis,” · 2024
Closest in time.