TPS-Drive uses an agent-centric tokenizer supervised by a frozen 3D detection head to purify VLM spatial representations, enabling better scene forecasting and lower collision rates on nuScenes and NAVSIM benchmarks.
arXiv preprint arXiv:2506.06218 (2025) 3, 9
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MVPruner is a two-stage dynamic token pruning technique that uses view diversity for initial budget allocation and instruction text for task-aligned selection, delivering 87.3% FLOPs reduction and 4.97x prefilling speedup while retaining 98.5% accuracy on DriveLM.
citing papers explorer
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TPS-Drive: Task-Guided Representation Purification for VLM-based Autonomous Driving
TPS-Drive uses an agent-centric tokenizer supervised by a frozen 3D detection head to purify VLM spatial representations, enabling better scene forecasting and lower collision rates on nuScenes and NAVSIM benchmarks.
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MVPruner: Dynamic Token Pruning for Accelerating Multi-view Vision-Language Models in Autonomous Driving
MVPruner is a two-stage dynamic token pruning technique that uses view diversity for initial budget allocation and instruction text for task-aligned selection, delivering 87.3% FLOPs reduction and 4.97x prefilling speedup while retaining 98.5% accuracy on DriveLM.