The arrival of autonomous vehicles (AVs) promises many great benefits, including increased
safety and reduced energy consumption, pollution, and congestion. However, these engines …

Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …

Federated learning (FL), as a distributed machine learning technology, allows large-scale
nodes to utilize local datasets for model training and sharing without revealing privacy …

In this paper, we study a new latency optimization problem for blockchain-based federated
learning (BFL) in multi-server edge computing. In this system model, distributed mobile …

Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the
automotive domain sector, offering promising solutions to address challenges such as traffic …

Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks
for their everyday functions on the road so that safety of passengers and vehicles can be …

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 …

In the heterogeneous vehicular networks (HetVNets), the roadside units (RUs) can exploit
the massive amounts of valuable data collected by vehicles to complete federated learning …

Bird's eye view (BEV) perception is becoming increasingly important in the field of
autonomous driving. It uses multi-view camera data to learn a transformer model that directly …

Autonomous vehicles (AVs) are getting popular because of their usage in a wide range of
applications like delivery systems, self-driving taxis, and ambulances. AVs utilize the power …