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About this paper
Dexter Le, Aybars Yunusoglu, Karn Tiwari, Murat Isik, I. Can Dikmen
A multimodal Large Language Model (LLM) framework is proposed for Intelligent Transportation Systems (ITS) to unify the processing of diverse sensor data, achieving an average accuracy of 91.33% across time-series, audio, and video modalities with real-time processing latencies under 13.5 ms on edge devices.
Key insights
- Leveraging pre-trained, modality-specific transformer models within a unified framework allows for high accuracy and reduced computational demands compared to training monolithic models from scratch.
- Converting structured time-series data into a text string format enables effective processing by BERT, demonstrating its versatility beyond traditional NLP tasks for dynamic system analysis.
- Orchestrating image captioning models (BLIP/CLIP) with a language model (T5) provides a robust method for visual understanding and textual interpretation of video data within a multimodal LLM framework.
Summary by alphaXiv.
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