Driving the Future of Connected Autonomous Mobility with Advanced AI and Multi-Modal Data Fusion: Plenary Talk

In this talk, we will explore the intersection of connected autonomous vehicles (CAVs), edge computing, multi-agent cooperation, computer vision, and large language models (LLMs) to tackle emerging challenges in intelligent transportation. Edge computing enables decentralized, low-latency processing, essential for real-time decision-making in tasks such as obstacle detection and navigation. Multi-agent cooperation, in this context, focuses on efficient sharing of radio and computational resources, ensuring that vehicles can dynamically allocate bandwidth and processing power to maintain high performance in communication and computation-heavy environments. Computer vision technologies enable vehicles to interpret complex surroundings through advanced scene analysis, enhancing safety and decision-making. Additionally, LLMs contribute to processing large amounts of contextual data, enabling more human-like reasoning and complex decision-making. Together, these technologies offer a holistcapproach to advancing the capabilities of autonomous vehicles, creating more robust, intelligent, and sustainable transportation systems.