Open Access

ARTICLE

Design of Virtual Driving Test Environment for Collecting and Validating Bad Weather SiLS Data Based on Multi-Source Images Using DCU with V2X-Car Edge Cloud

Sun Park^*, JongWon Kim

AI Graduate School, GIST, Gwangju, 61005, Republic of Korea

* Corresponding Author: Sun Park. Email:

(This article belongs to the Special Issue: Advancing Edge-Cloud Systems with Software-Defined Networking and Intelligence-Driven Approaches)

Computers, Materials & Continua 2026, 86(3), 15 https://doi.org/10.32604/cmc.2025.072865

Received 05 September 2025; Accepted 24 October 2025; Issue published 12 January 2026

Abstract

In real-world autonomous driving tests, unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur. Conducting actual test drives under various weather conditions may also lead to dangerous situations. Furthermore, autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS. Driving simulators, which replicate driving conditions nearly identical to those in the real world, can drastically reduce the time and cost required for market entry validation; consequently, they have become widely used. In this paper, we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images. The proposed method generates a virtual testing environment that incorporates various events, including weather, time of day, and moving objects, that cannot be easily verified in real-world autonomous driving tests. By setting up scenario-based virtual environment events, multi-source image analysis and verification using real-world DCUs (Data Concentrator Units) with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations. We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.

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Keywords

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