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Enterprise AI Analysis: Supervised Token Reduction for Multi-modal LLMs toward efficient end-to-end autonomous driving

Deep Analysis & Enterprise Applications

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STORM: Efficient Autonomous Driving

STORM introduces a novel approach to enhance the efficiency of end-to-end autonomous driving systems by intelligently reducing visual tokens processed by Multi-modal Large Language Models (MLLMs). This is critical for real-time operation in resource-constrained vehicles, ensuring that essential information for safe driving is retained.

Key Technical Components

The framework leverages a lightweight importance predictor with sliding windows to score visual token relevance, an auxiliary path for generating pseudo-supervision signals from full MLLM attention scores, and an Anchor-Context Merging (ACM) module to efficiently merge less important context tokens into critical anchor tokens, minimizing redundancy.

Unprecedented Efficiency & Performance

STORM achieves a significant reduction in computational cost, up to 30x, compared to state-of-the-art MLLMs, while maintaining comparable driving performance. This enables real-time E2E autonomous driving on standard GPUs, making MLLM-based solutions practical for commercial deployment.

30X Reduction in Computational Cost for MLLM-based Autonomous Driving

Enterprise Process Flow

Data Collection & Preprocessing
Importance Prediction (STORM)
Token Reduction (ACM)
LLM Inference
Control Commands Generation

STORM vs. State-of-the-Art MLLMs

Feature STORM SOTA LMDrive
Token Reduction Method
  • Supervised, Importance-aware
  • Anchor-Context Merging
  • Heuristic (Q-Former)
  • Fixed queries
Performance on LangAuto
  • Maintains All-token Performance
  • Superior Driving Score
  • Often Degraded Performance
  • Lower Driving Score
Computational Cost
  • Up to 30x Reduction in FLOPs
  • Real-time on Standard GPU
  • High Computational Cost
  • Challenging for Real-time

Case Study: Real-time Autonomous Driving

STORM enables real-time E2E driving on a standard GPU by significantly reducing computational overhead. This innovation makes MLLMs viable for in-vehicle deployment without sacrificing safety or performance, translating to safer, more responsive AI-driven vehicles in complex and unexpected scenarios.

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