AI‑Based Traffic Flow Prediction Models Using Real-Time Data

Abstract

Vision Language Models (VLMs) have quickly come to dominate as a ground-breaking type of multimodal artificial intelligence systems with the ability to comprehend not only visual but also linguistic input. Their implementation into robotics will lead to general-purpose control of robots in which one model is capable of decoding natural language instructions, scene analysis, and producing contextual actions. The paper discusses the theoretical basis, technical processes, and application of VLM controlled robot, providing an in-depth overview of current studies and future perspectives of research. In a discussion of transformer architectures, multimodal encodings and robot behavior generation pipes, the paper identifies how VLMs can enable robots to reason like humans. Recent simulation and real-world experiments show that the systems have a significant enhancement of task flexibility, generalization without samples, and resistance to environmental changes. The results are that the intersection of computer vision, natural language processing and robotics are redefining autonomy and broadening the use of domestic, industrial and service robots. Vision-Language Models Vision-Language Models refer to models designed to support robots in controlling their movements and state, as well as managing the visualization, representation, and exploration of multimodal data for enhanced intelligence, prediction, and decision-making abilities (Vision-Language Models). Robot Control Multimodal AI Multimodal AI (Vision-Language Models) Multimodal data-visualization, -representation, and -exploration Multimodal data-visualization and -representation Multimodal data-visualization refers to a visual.