Split Computing in Autonomous Mobility for Efficient Semantic Segmentation using Transformers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00389862" target="_blank" >RIV/68407700:21230/25:00389862 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CIETES63869.2025.10995197" target="_blank" >http://dx.doi.org/10.1109/CIETES63869.2025.10995197</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CIETES63869.2025.10995197" target="_blank" >10.1109/CIETES63869.2025.10995197</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Split Computing in Autonomous Mobility for Efficient Semantic Segmentation using Transformers

Popis výsledku v původním jazyce

With the advancement of deep learning in Connected Autonomous Vehicles (CAVs), real-time semantic segmentation has emerged as a crucial task. The integration of semantic segmentation vision transformers into CAV perception systems capitalizes on the efficiency of the vision transformers and their capacity to capture a global context. To further optimize transformer's performance, we propose the integration of a Split Computing (SC) into the transformer architecture to enable the processing of the computation related to transformers to be distributed between the CAVs and an Edge Computing Server (ECS). The objective of our work is to evaluate SC efficiency in minimizing latency of intensive computational tasks of the transformers with minimal loss in semantic segmentation's accuracy. Simulations demonstrate that vision transformer architectures are well-suited for the SC integration and outperform both ECSOnly and CAV-Only baseline approaches in various scenarios, offering an acceptable latency-accuracy trade-offs. Specifically, our findings indicate that SC outperforms baseline methods in terms of latency by up to 79.36%, incurring a minor accuracy reduction. This makes them particularly suitable for applications where real-time processing and minimal latency are critical considerations.

Název v anglickém jazyce

Split Computing in Autonomous Mobility for Efficient Semantic Segmentation using Transformers

Popis výsledku anglicky

With the advancement of deep learning in Connected Autonomous Vehicles (CAVs), real-time semantic segmentation has emerged as a crucial task. The integration of semantic segmentation vision transformers into CAV perception systems capitalizes on the efficiency of the vision transformers and their capacity to capture a global context. To further optimize transformer's performance, we propose the integration of a Split Computing (SC) into the transformer architecture to enable the processing of the computation related to transformers to be distributed between the CAVs and an Edge Computing Server (ECS). The objective of our work is to evaluate SC efficiency in minimizing latency of intensive computational tasks of the transformers with minimal loss in semantic segmentation's accuracy. Simulations demonstrate that vision transformer architectures are well-suited for the SC integration and outperform both ECSOnly and CAV-Only baseline approaches in various scenarios, offering an acceptable latency-accuracy trade-offs. Specifically, our findings indicate that SC outperforms baseline methods in terms of latency by up to 79.36%, incurring a minor accuracy reduction. This makes them particularly suitable for applications where real-time processing and minimal latency are critical considerations.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LUASK22064" target="_blank" >LUASK22064: Prediktivní alokace výpočetních prostředků pro autonomní řízení na hraně sítě</a><br>

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 statě ve sborníku

2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES)

ISBN

979-8-3315-0825-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Trondheim

Datum konání akce

17. 3. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001551760800017