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XPENG-Peking University Collaborative Research Accepted by AAAI 2026: Introducing a Novel Visual Token Pruning Framework for Autonomous Driving
XPENG, in collaboration with Peking University, has had its paper "FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning" accepted by AAAI 2026, one of the world's top conferences in artificial intelligence. AAAI 2026 received 23,680 submissions, with only 4,167 papers accepted, an acceptance rate of just 17.6%.
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[{"type":"list","items":[{"val":[{"type":"text","content":"XPENG-PKU Research Breakthrough: XPENG, in collaboration with Peking University, has developed FastDriveVLA—a novel visual token pruning framework that enables autonomous driving AI to "drive like a human" by focusing only on essential information, achieving a 7.5x reduction in computational load.","length":308,"tagName":"p"}]},{"val":[{"type":"text","content":"Top-Tier AI Recognition: The research has been accepted by AAAI 2026, one of the world's premier AI conferences, which had a highly selective acceptance rate of just 17.6% this year.","length":186,"tagName":"p"}]},{"val":[{"type":"text","content":"Accelerating L4 Autonomy: This achievement underscores XPENG's full-stack capabilities in AI-driven mobility and advances the industry toward efficient, scalable deployment of next-generation autonomous driving systems.","length":223,"tagName":"p"}]}],"tagName":"ul","bulletedList":true,"length":717,"olType":false},{"type":"text","content":"GUANGZHOU, China, Dec. 28, 2025 /PRNewswire/ -- XPENG, in collaboration with Peking University, has had its paper "FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning" accepted by AAAI 2026, one of the world's top conferences in artificial intelligence. AAAI 2026 received 23,680 submissions, with only 4,167 papers accepted, an acceptance rate of just 17.6%.","length":417,"tagName":"p"},{"type":"text","content":"The paper introduces FastDriveVLA, an efficient visual token pruning framework specifically designed for end-to-end autonomous driving Vision-Language-Action (VLA) models. This work offers a new approach to visual token pruning by enabling AI to "drive like a human", focusing only on essential visual information while filtering out irrelevant data.","length":360,"tagName":"p"},{"type":"text","content":"As AI large models evolve rapidly, VLA models are being widely adopted in end-to-end autonomous driving systems due to their strong capabilities in complex scene understanding and action reasoning. These models encode images into large numbers of visual tokens, which serve as the foundation for the model to "see" the world and make driving decisions. However, processing large numbers of tokens increases computational load onboard the vehicle, impacting inference ...