Autoregressive Meta-Actions for Unified Controllable Trajectory Generation

• URL: http://arxiv.org/abs/2505.23612v1
• Date: Thu, 29 May 2025 16:19:59 GMT
• Title: Autoregressive Meta-Actions for Unified Controllable Trajectory Generation
• Authors: Jianbo Zhao, Taiyu Ban, Xiyang Wang, Qibin Zhou, Hangning Zhou, Zhihao Liu, Mu Yang, Lei Liu, Bin Li,
• Abstract summary: Controllable trajectory generation is crucial for autonomous driving systems.<n>Existing frameworks rely on invariant meta-actions assigned over fixed future time intervals.<n>We introduce Autoregressive Meta-Actions, an approach integrated into autoregressive trajectory generation frameworks.
• Score: 10.123353592943968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Controllable trajectory generation guided by high-level semantic decisions, termed meta-actions, is crucial for autonomous driving systems. A significant limitation of existing frameworks is their reliance on invariant meta-actions assigned over fixed future time intervals, causing temporal misalignment with the actual behavior trajectories. This misalignment leads to irrelevant associations between the prescribed meta-actions and the resulting trajectories, disrupting task coherence and limiting model performance. To address this challenge, we introduce Autoregressive Meta-Actions, an approach integrated into autoregressive trajectory generation frameworks that provides a unified and precise definition for meta-action-conditioned trajectory prediction. Specifically, We decompose traditional long-interval meta-actions into frame-level meta-actions, enabling a sequential interplay between autoregressive meta-action prediction and meta-action-conditioned trajectory generation. This decomposition ensures strict alignment between each trajectory segment and its corresponding meta-action, achieving a consistent and unified task formulation across the entire trajectory span and significantly reducing complexity. Moreover, we propose a staged pre-training process to decouple the learning of basic motion dynamics from the integration of high-level decision control, which offers flexibility, stability, and modularity. Experimental results validate our framework's effectiveness, demonstrating improved trajectory adaptivity and responsiveness to dynamic decision-making scenarios. We provide the video document and dataset, which are available at https://arma-traj.github.io/.

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