@inproceedings{f0c575abe0674c4fb587f1a7df179e54,
title = "Poster: Multi-Camera Interoperable Emulation Framework Using Embedded Edge-Cloud AI Computing for Autonomous Vehicle Driving",
abstract = "The growth of autonomous driving technology is accelerating. However, complete autonomous driving has not been implemented yet. This paper proposes a multi-camera interoperable emulation framework for developing autonomous vehicle driving. We implement two components of the Advanced Driver Assistance System (ADAS). The vehicle adjusts its speed based on the distance from the object and stays in its lane. Smart Cruise Control (SCC) and Lane Keeping Assist (LKA) are. These two systems are remotely controlled in our framework. As a result, developing, applying, and simulating algorithms will be more convenient, and this can protect drivers from accidents caused by incomplete algorithms during simulations. Moreover, these systems can relieve the drivers' burden and fatigue during real driving and prevent dangerous situations that can occur due to other vehicles or pedestrians.",
keywords = "ADAS, Multi-camera, Remote control",
author = "Hyunjoong Lee and Daejin Park",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th IEEE Vehicular Networking Conference, VNC 2024 ; Conference date: 29-05-2024 Through 31-05-2024",
year = "2024",
doi = "10.1109/VNC61989.2024.10575966",
language = "English",
series = "IEEE Vehicular Networking Conference, VNC",
publisher = "IEEE Computer Society",
pages = "251--252",
editor = "Susumu Ishihara and Hiroshi Shigeno and Onur Altintas and Takeo Fujii and Raphael Frank and Florian Klingler and Tobias Hardes and Tobias Hardes",
booktitle = "2024 IEEE Vehicular Networking Conference, VNC 2024",
address = "United States",
}