Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.084571
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

An Edge-Assisted Internet-of-Vehicles Computing Framework for Fair Tail-Risk Allocation in Cooperative Autonomous Driving

Shih-Lin Lin*
Graduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua City, Taiwan
* Corresponding Author: Shih-Lin Lin. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.084571

Received 25 April 2026; Accepted 18 June 2026; Published online 07 July 2026

Abstract

Connected automated driving increasingly relies on cooperative perception from onboard sensors, roadside units (RSUs), smart traffic lights, and vehicle-to-everything (V2X) links, but communication uncertainty can concentrate residual risk on vulnerable road users (VRUs). This study proposes an Ethical-Improved risk-allocation objective for edge-assisted Internet of Vehicles (IoV) cooperative autonomous driving. The objective internalizes responsibility as a bounded risk weight, normalizes equality and maximin terms, and adds explicit VRU tail-risk and VRU/Ego ratio penalties. The evaluation is organized into two strictly separated tracks. In the planner-objective track, Ethical-Improved reduces physical collision rate, aggregate harm, inequality, and VRU tail risk relative to Standard, Selfish, and Ethical-Orig baselines. In the communication-aware IoV track, edge-assisted cooperative perception achieves the best within-track collision-proxy and normalized-harm performance under sampled packet delivery ratio (PDR), age of information (AoI), RSU coverage, perception confidence, and edge delay conditions. Physical collision rate and communication-conditioned collision proxy are reported separately and are not numerically compared across tracks. The results support auditable tail-risk regularization as a controlled-simulator design principle for fairer cooperative autonomous driving, while external validation remains necessary before deployment-oriented claims can be made.

Keywords

Internet of vehicles; edge-assisted cooperative perception; vehicle-road cooperation; autonomous driving; fairness-aware trajectory planning; tail-risk regularization; vulnerable road user protection

  • 141

    View

  • 20

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link