XPENG and Peking University Develop Efficient Visual Token Pruning for End-to-End Autonomous Driving
XPENG, in collaboration with Peking University, has achieved acceptance of its research paper at AAAI 2026, introducing FastDriveVLA—an advanced visual token pruning framework tailored for end-to-end Vision-Language-Action (VLA) models in autonomous driving. This innovation significantly reduces computational demands while preserving planning accuracy, enabling more efficient onboard processing in electric vehicles equipped with advanced driver-assistance systems. The development highlights XPENG’s progress in optimizing AI large models for real-world deployment in intelligent EVs.