相关论文: A V2X-based Privacy Preserving Federated Measuring…

Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling…

Due to the limitations of a single autonomous vehicle, Cellular Vehicle-to-Everything (C-V2X) technology opens a new window for achieving fully autonomous driving through sensor information sharing. However, real-world datasets supporting…

With the rapid development of communication technology, connected vehicles (CV) have the potential, through the sharing of data, to enhance vehicle safety and reduce vehicle energy consumption and emissions. Numerous research efforts are…

The role of deep learning (DL) in robotics has significantly deepened over the last decade. Intelligent robotic systems today are highly connected systems that rely on DL for a variety of perception, control, and other tasks. At the same…

Federated Learning enables visual models to be trained on-device, bringing advantages for user privacy (data need never leave the device), but challenges in terms of data diversity and quality. Whilst typical models in the datacenter are…

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of…

Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In the FEEL system, vehicles upload data to the edge servers,…

Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices. However, the leading optimization algorithm in…

Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm of ML that facilitates…

Significant progress has been made towards deploying Vehicle-to-Everything (V2X) technology. Integrating V2X with 5G has enabled ultra-low latency and high-reliability V2X communications. However, while communication performance has…

To leverage the vast amounts of onboard data while ensuring privacy and security, federated learning (FL) is emerging as a promising technology for supporting a wide range of vehicular applications. Although FL has great potential to…

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust cooperative perception framework with V2X communication using a…

This book offers a hands-on introduction to building and understanding federated learning (FL) systems. FL enables multiple devices -- such as smartphones, sensors, or local computers -- to collaboratively train machine learning (ML)…

Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to…

The emerging technology of Vehicle-to-Vehicle (V2V) communication over vehicular ad hoc networks promises to improve road safety by allowing vehicles to autonomously warn each other of road hazards. However, research on other transportation…

As autonomous vehicles edge closer to widespread adoption, enhancing road safety through collision avoidance and minimization of collateral damage becomes imperative. Vehicle-to-everything (V2X) technologies, which include…

Imitation learning is a promising approach for training autonomous vehicles (AV) to navigate complex traffic environments by mimicking expert driver behaviors. While existing imitation learning frameworks focus on leveraging expert…

Intelligent Transportation System (ITS) has become one of the essential components in Industry 4.0. As one of the critical indicators of ITS, efficiency has attracted wide attention from researchers. However, the next generation of urban…

In today's era, autonomous vehicles demand a safety level on par with aircraft. Taking a cue from the aerospace industry, which relies on redundancy to achieve high reliability, the automotive sector can also leverage this concept by…

Federated Learning (FL) is a privacy-protected machine learning paradigm that allows model to be trained directly at the edge without uploading data. One of the biggest challenges faced by FL in practical applications is the heterogeneity…