Xianpeng Lang

The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning

Titong Jiang, X. S. Jiang, Yuan Ma, Xin Wen et autres

We present LightVLA, a simple yet effective differentiable token pruning framework for vision-language-action (VLA) models. While VLA models have shown impressive capability in executing real-world robotic tasks, their deployment on resource-constrained platforms is often bottlenecked by the heavy attention-based computation over large …

Generalizing Motion Planners with Mixture of Experts for Autonomous Driving

Qiao Sun, Huimin Wang, Jiahao Zhan, Xin Wen et autres

Large real-world driving datasets have sparked significant research into various aspects of learning-based motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. In this paper, we review and benchmark previous methods. Experiments show …

cn, us (code pays fourni par la source)

TransDiffuser: Diverse Trajectory Generation with Decorrelated Multi-modal Representation for End-to-end Autonomous Driving

Xuefeng Jiang, Yuan Ma, Pengxiang Li, Ling Xu et autres

In recent years, diffusion models have demonstrated remarkable potential across diverse domains, from vision generation to language modeling. Transferring its generative capabilities to modern end-to-end autonomous driving systems has also emerged as a promising direction. However, existing diffusion-based trajectory generative models often …

TokenFLEX: Unified VLM Training for Flexible Visual Tokens Inference

Junshan Hu, Jialiang Mao, Zhikang Liu, Zhongpu Xia et autres

Conventional Vision-Language Models(VLMs) typically utilize a fixed number of vision tokens, regardless of task complexity. This one-size-fits-all strategy introduces notable inefficiencies: using excessive tokens leads to unnecessary computational overhead in simpler tasks, whereas insufficient tokens compromise fine-grained visual comprehension in more complex …

Generalizing Motion Planners with Mixture of Experts for Autonomous Driving

Qiao Sun, Huimin Wang, Jiahao Zhan, Fan Nie et autres

Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. These planners promise better generalizations on complicated and few-shot cases than …

PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning

Yupeng Zheng, Zebin Xing, Qichao Zhang, Bu Jin et autres

Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios but struggle to generalize to long-tailed situations. Meanwhile, learning-based methods have yet to achieve superior performance over rule-based approaches in …