A dynamic fuzzy video compression control algorithm for wireless Advanced Driver Assistance Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257739" target="_blank" >RIV/61989100:27240/25:10257739 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0952197625008152" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197625008152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2025.110815" target="_blank" >10.1016/j.engappai.2025.110815</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A dynamic fuzzy video compression control algorithm for wireless Advanced Driver Assistance Systems

Popis výsledku v původním jazyce

As video-based Advanced Driver Assistance Systems (ADAS) become integral to modern vehicle safety, the demand for reliable, high-performance wireless solutions for retrofitting vehicles have grown. This study introduces a wireless ADAS that employs a novel dynamic video compression control algorithm, integrating a hardware-based Motion Joint Photographic Experts Group (MJPEG) compression engine with adaptive fuzzy logic control strategies. The system dynamically adjusts video compression levels based on real-time conditions such as wireless data rates and available bandwidth, addressing key challenges in maintaining video quality and minimizing latency in wireless environments. The adaptive control is governed by two distinct fuzzy control strategies: Fuzzy Rule-Based (FRB) and Evolutionary Fuzzy Rules (EFR). Both strategies were optimized using nature-inspired algorithms, including Differential Evolution (DE), Particle Swarm Optimization (PSO), and Genetic Programming (GP). Among these, the EFR-based control was found to offer the best overall performance. Key performance indicators such as compression efficiency, latency, and throughput rates were thoroughly evaluated. Experimental results demonstrated that the EFR-based system provided up to a 35% improvement in compression efficiency compared to traditional methods, reduced video latency by approximately 20%, and optimized data throughput. Furthermore, the EFR-based control showcased enhanced generalization capabilities, outperforming FRB-based control under previously unobserved conditions, which is critical for real-world vehicular applications where network conditions may vary significantly. The implementation of artificial intelligence in the form of EFR significantly enhanced the system&apos;s ability to adapt to varying data rates and environmental conditions, making it a promising solution for real-time video compression in computationally constrained embedded systems. © 2025 The Authors

Název v anglickém jazyce

A dynamic fuzzy video compression control algorithm for wireless Advanced Driver Assistance Systems

Popis výsledku anglicky

As video-based Advanced Driver Assistance Systems (ADAS) become integral to modern vehicle safety, the demand for reliable, high-performance wireless solutions for retrofitting vehicles have grown. This study introduces a wireless ADAS that employs a novel dynamic video compression control algorithm, integrating a hardware-based Motion Joint Photographic Experts Group (MJPEG) compression engine with adaptive fuzzy logic control strategies. The system dynamically adjusts video compression levels based on real-time conditions such as wireless data rates and available bandwidth, addressing key challenges in maintaining video quality and minimizing latency in wireless environments. The adaptive control is governed by two distinct fuzzy control strategies: Fuzzy Rule-Based (FRB) and Evolutionary Fuzzy Rules (EFR). Both strategies were optimized using nature-inspired algorithms, including Differential Evolution (DE), Particle Swarm Optimization (PSO), and Genetic Programming (GP). Among these, the EFR-based control was found to offer the best overall performance. Key performance indicators such as compression efficiency, latency, and throughput rates were thoroughly evaluated. Experimental results demonstrated that the EFR-based system provided up to a 35% improvement in compression efficiency compared to traditional methods, reduced video latency by approximately 20%, and optimized data throughput. Furthermore, the EFR-based control showcased enhanced generalization capabilities, outperforming FRB-based control under previously unobserved conditions, which is critical for real-world vehicular applications where network conditions may vary significantly. The implementation of artificial intelligence in the form of EFR significantly enhanced the system&apos;s ability to adapt to varying data rates and environmental conditions, making it a promising solution for real-time video compression in computationally constrained embedded systems. © 2025 The Authors

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Svazek periodika

153

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

nestránkováno

Kód UT WoS článku

001480984000001

EID výsledku v databázi Scopus

2-s2.0-105003378617