LLM-Empowered Multimodal Fusion Framework for Autonomous Driving: Semantic Enhancement and Channel-Adaptive Design

2026-07-02 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The LM-SCIP (Large Language Model-centric Semantic-layer Channel-aware Integrated Perception) framework enhances autonomous driving by addressing dynamic input quality in vision-radar fusion. This framework re-frames static data fusion into channel-aware semantic reasoning, utilizing an LLM as a central core to fuse local visual streams with quality-varying external radar data, covering perception-blind spots. LM-SCIP integrates a hierarchical radar-vision encoder with a Channel-Adaptive Semantic Module (CASM) that dynamically gates radar features via a "Channel Prompt." A parameter-efficient, LoRA-tuned LLM, combined with a heterogeneous Mixture-of-Experts (H-MoE), arbitrates between visual and channel-conditioned radar contexts. A decoupled multi-task decoder then handles localization, trajectory forecasting, and image reconstruction. Experimental validation on nuScenes demonstrated a 40.0% reduction in localization RMSE compared to a vision-only baseline, while on VIRAT, the model achieved a 0.214m localization RMSE and 0.179m minFDE (k=1).

Key takeaway

For autonomous driving engineers designing robust perception systems, the LM-SCIP framework offers a compelling approach to overcome dynamic input quality challenges. You should consider integrating LLM-centric, channel-adaptive fusion architectures to dynamically arbitrate between sensor streams like vision and radar. This method significantly improves localization accuracy, as demonstrated by a 40.0% RMSE reduction on nuScenes, ensuring more reliable performance in adverse conditions and perception-blind spots.

Key insights

LLM-centric LM-SCIP framework dynamically fuses vision and radar for robust autonomous driving perception, adapting to varying channel quality.

Principles

• Dynamic channel-aware semantic reasoning improves multimodal fusion.
• LLMs can serve as central reasoning cores for sensor arbitration.
• Heterogeneous MoE enhances context-aware decision-making.

Method

LM-SCIP couples a hierarchical radar-vision encoder with a Channel-Adaptive Semantic Module (CASM) to gate radar features. A LoRA-tuned LLM and H-MoE arbitrate cues, followed by a multi-task decoder for outputs.

In practice

• Implement channel-adaptive modules for dynamic sensor input.
• Integrate LoRA-tuned LLMs for multimodal sensor fusion.
• Utilize MoE architectures for context-dependent arbitration.

Topics

• Autonomous Driving
• Multimodal Sensor Fusion
• Large Language Models
• Vision-Radar Perception
• Channel-Adaptive Design
• Localization Accuracy

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.