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PRAM-R: A Perception-Reasoning-Action-Memory Framework with LLM-Guided Modality Routing for Adaptive Autonomous Driving
arXiv – CS AI|Yi Zhang, Xian Zhang, Saisi Zhao, Yinglei Song, Chengdong Wu, Nenad Petrovic, Alois Knoll|
🤖AI Summary
PRAM-R introduces a new AI framework for autonomous driving that uses LLM-guided modality routing to adaptively select sensors based on environmental conditions. The system achieves 6.22% modality reduction while maintaining trajectory accuracy, demonstrating efficient resource management in multimodal perception systems.
Key Takeaways
- →PRAM-R framework uses dual-loop design with fast reactive perception and slow deliberative reasoning for autonomous driving.
- →LLM router dynamically selects and weights sensor modalities based on environmental context and diagnostics.
- →System achieves 87.2% reduction in routing oscillations through hysteresis-based stabilization.
- →Real-world validation shows 6.22% modality reduction with maintained trajectory accuracy on nuScenes dataset.
- →Hierarchical memory module enables temporal consistency and long-term adaptation in multimodal perception.
Read Original →via arXiv – CS AI
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