output
20192026
most citedArchitectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles

185 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing cs.NIShow all

11 papers · 1 filter

cs.NI2026★ 10 cited

Support of Teleoperated Driving with 5G Networks

M. Carmen Lucas-Estañ, Baldomero Coll-Perales, Mohammad Irfan Khan +4

Teleoperated driving (ToD) can support autonomous driving under complex or unexpected traffic scenarios that an autonomous vehicle may not understand or be able to handle. In ToD,…

cs.NI2026★ 1 cited

FORESEE: A Cooperative Lane Change Model for Connected and Automated Driving

Rafael Molina-Masegosa, Sergei S. Avedisov, Miguel Sepulcre +3

This paper presents FORESEE, a novel cooperative lane change model for connected and automated driving. FORESEE leverages Vehicle-to-Everything (V2X) data to anticipate traffic con…

cs.NI2026★ 13 cited

Direct-V2X Support with 5G Network-based Communications: Performance, Challenges and Solutions

M. C. Lucas-Estañ, B. Coll-Perales, T. Shimizu +5

This study analyzes the feasibility of supporting critical V2X services using 5G network-based Vehicle-to-Network-to-Vehicle (V2N2V) communications. The study evaluates the end-to-…

cs.NI2026★ 1 cited

5G Network Architecture and Configuration Choices to Support Teleoperated Driving at Scale

M. C. Lucas-Estañ, B. Coll-Perales, M. I. Khan +4

Teleoperated driving (ToD) enables the remote driving or control of vehicles. For this purpose, vehicles must transmit video feeds to the ToD control center so that the remote oper…

cs.NI2026

Impact of ADAS and V2X Penetration Rates on Cooperative Active Safety

M. C. Lucas-Estañ, B. Coll-Perales, M. I. Khan +2

A major driver of connected and automated driving is cooperative active safety. The effectiveness of cooperative safety applications depends on the ability of vehicles to detect tr…

cs.NI2023★ 35 cited

Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices

Xiaolong Tu, Anik Mallik, Dawei Chen +4

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often…

cs.NI2022★ 53 cited

An Analytical Latency Model and Evaluation of the Capacity of 5G NR to Support V2X Services using V2N2V Communications

M. C. Lucas-Estañ, B. Coll-Perales, T. Shimizu +5

5G has been designed to support applications such as connected and automated driving. To this aim, 5G includes a highly flexible New Radio (NR) interface that can be configured to…

cs.NI2022★ 91 cited

End-to-End V2X Latency Modeling and Analysis in 5G Networks

B. Coll-Perales, M. C. Lucas-Estañ, T. Shimizu +5

5G networks provide higher flexibility and improved performance compared to previous cellular technologies. This has raised expectations on the possibility to support advanced V2X…

cs.NI2021★ 17 cited

The Transdimensional Poisson Process for Vehicular Network Analysis

Jeya Pradha Jeyaraj, Martin Haenggi, Ahmed Hamdi Sakr +1

A comprehensive vehicular network analysis requires modeling the street system and vehicle locations. Even when Poisson point processes (PPPs) are used to model the vehicle locatio…

cs.NI2020★ 185 cited

Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles

Haoxin Wang, Tingting Liu, BaekGyu Kim +4

As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can p…

cs.NI2020★ 9 cited

Securing Vehicle-to-Everything (V2X) Communication Platforms

Monowar Hasan, Sibin Mohan, Takayuki Shimizu +1

Modern vehicular wireless technology enables vehicles to exchange information at any time, from any place, to any network -- forms the vehicle-to-everything (V2X) communication pla…