Federated learning for connected and automated vehicles: A survey of existing approaches and challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
A survey of federated learning for connected and automated vehicles
Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the
automotive domain sector, offering promising solutions to address challenges such as traffic …
automotive domain sector, offering promising solutions to address challenges such as traffic …
5g on the roads: Latency-optimized federated analytics in the vehicular edge
Coordination among vehicular actors becomes increasingly important at the dawn of
autonomous driving. With communication serving as the basis for this process, latency …
autonomous driving. With communication serving as the basis for this process, latency …
Resource-aware multi-criteria vehicle participation for federated learning in Internet of vehicles
J Wen, J Zhang, Z Zhang, Z Cui, X Cai, J Chen - Information Sciences, 2024 - Elsevier
Federated learning (FL), as a safe distributed training mode, provides strong support for the
edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and …
edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and …
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
Roadside unit (RSU) can significantly improve the safety and robustness of autonomous
vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a …
vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a …
Variable Weight Combination Model for Lane-changing Prediction of Human-Driven Vehicle
X Wang, Y Zan, J Zheng, E Wenjuan… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Accurate lane-changing prediction is important to Advanced Driver Assistance Systems
(ADAS). However, the prediction accuracy declines when the lateral motion is ambiguous …
(ADAS). However, the prediction accuracy declines when the lateral motion is ambiguous …
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles
C Quéméneur, S Cherkaoui - arXiv preprint arXiv:2406.03611, 2024 - arxiv.org
The Internet of Vehicles (IoV) emerges as a pivotal component for autonomous driving and
intelligent transportation systems (ITS), by enabling low-latency big data processing in a …
intelligent transportation systems (ITS), by enabling low-latency big data processing in a …
FedVANET-TP: Federated Trajectory Prediction Model for VANETs
In recent years, deep learning techniques have been employed within Intelligent
Transportation Systems (ITS) to outperform classical trajectory prediction models, aiming for …
Transportation Systems (ITS) to outperform classical trajectory prediction models, aiming for …
QBDD: Quantum-resistant blockchain-assisted deep data deduplication protocol for vehicular crowdsensing system
J Li, Q Nong, Z Liu - Computer Networks, 2024 - Elsevier
Abstract Vehicular Crowdsensing System (VCS) has emerged as a promising paradigm for
alleviating traffic congestion and improving driving safety due to its convenient collection …
alleviating traffic congestion and improving driving safety due to its convenient collection …
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain
W Chen, L Jia, Y Zhou, Q Ren - arXiv preprint arXiv:2407.19428, 2024 - arxiv.org
Federated learning combined with blockchain empowers secure data sharing in
autonomous driving applications. Nevertheless, with the increasing granularity and …
autonomous driving applications. Nevertheless, with the increasing granularity and …