Split Computing in Autonomous Mobility for Efficient Semantic Segmentation using Transformers

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Split Computing in Autonomous Mobility for Efficient Semantic Segmentation using Transformers

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/LUASK22064" target="_blank" >LUASK22064: Predictive allocation of edge computing resources for autonomous driving</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Article name in the collection

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

ISBN

979-8-3315-0825-8

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

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Publisher name

IEEE

Place of publication

Piscataway

Event location

Trondheim

Event date

Mar 17, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001551760800017