VLA

中文版本：阅读中文版 Introduction Figure from Alpamayo-R1: Bridging Reasoning and Action Prediction for Autonomous Driving End-to-end autonomous driving has made significant progress in recent years, yet deploying Vision-Language-Action (VLA) models in real-world driving scenarios remains challenging. The basic difficulties are fourfold. First, multi-frame temporal understanding requires the model to extract decision-relevant changes from highly redundant consecutive observations, rather than merely processing static snapshots. Second, driving decisions must be causal: the model must model why a particular action is taken, not just learn statistical correlations between situations and actions. Third, predicted trajectories must satisfy kinematic and dynamic constraints while remaining multi-modal and efficient enough for real-time inference. Fourth, the reasoning process must be tightly aligned with action output—reasoning should not be a post-hoc rationalization but must be verifiable by and constrained by the actual actions taken. ...

中文版本：阅读中文版 Introduction The trajectory of autonomous driving architecture has undergone a paradigm shift: from the classical modular pipeline (perception →\to prediction →\to planning →\to control) toward end-to-end systems that map sensory inputs directly to driving actions. This transition is not merely an engineering convenience—it reflects a deep recognition that modular interfaces impose information bottlenecks and that joint optimization across the full stack can yield emergent capabilities invisible to individually optimized modules. ...