Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning
arXiv:2507.19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing. Existing studies often overlook the intricate interdependencies between missions and the costs associated with offloading tasks to edge servers, leading to suboptimal decision-making. To bridge this gap, we introduce Oranits, a novel system model that explicitly accounts for mission dependencies and off
The increasing complexity of autonomous systems and mobile edge computing in intelligent transportation necessitates advanced AI solutions for efficient resource management and task offloading.
This development addresses critical challenges in latency and computational efficiency for ITS, which is vital for the deployment and reliability of autonomous vehicles and smart city infrastructure.
The explicit consideration of mission dependencies and offloading costs through models like Oranits will improve decision-making in Open RAN-based ITS, moving beyond current suboptimal approaches.
- · Autonomous Vehicle Manufacturers
- · Smart City Developers
- · Telecommunications Providers
- · AI/ML Software Developers
Improved efficiency and reliability of intelligent transportation systems through better task management.
Accelerated adoption and broader deployment of autonomous vehicles and smart infrastructure due to enhanced performance and safety.
New regulatory frameworks and industry standards emerging to manage complex AI-driven network optimization in critical infrastructure sectors.
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