Diffusion

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. ...

中文版本：阅读中文版 Figure from DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving Autoregressive (AR) trajectory generation — predicting driving trajectories as sequences of discrete tokens, much like language models predict text — has emerged as a powerful paradigm for end-to-end autonomous driving. But how do we turn continuous trajectories into discrete tokens? How do we ensure the tokenized representation preserves enough fidelity for planning? And how does the AR paradigm combine with diffusion and reinforcement learning to produce state-of-the-art results? This article walks through the complete pipeline, from tokenization theory to RL post-training. ...

中文版本：阅读中文版 Figure from DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving The trajectory planner is the decision-making core of an autonomous driving system. Its task: given the current scene, output a future trajectory that is safe, comfortable, and efficient. Most production systems today use some form of regression — minimizing the distance between predicted and ground-truth trajectories. Yet a growing body of research and engineering evidence suggests this approach has a basic flaw: it assumes the feasible set is convex when it is emphatically not. This article lays out the first-principles argument for why generative approaches (diffusion, autoregressive) are necessary paradigm shifts, not merely improvements. ...