Instruction-driven framework of Traframework of TrafficPerceiver for traffic scene understanding and segmentation (IMAGE)
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The TrafficPerceiver framework integrates text instructions and visual inputs within a multimodal large language model to perform both traffic scene understanding and target-oriented segmentation. Natural language queries are encoded together with image features, enabling instruction-driven reasoning and pixel-level mask prediction. The framework is further optimized using reinforcement learning to improve robustness under challenging traffic conditions such as rain, blur, and nighttime scenarios.
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Communications in Transportation Research
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