離散擴散在自動駕駛中的反射式視覺-語言-動作模型應用

摘要

端到端（E2E）解决方案已成为自动驾驶系统的主流方法，其中视觉-语言-动作（VLA）模型代表了一种新范式，它利用预训练的多模态知识从视觉-语言模型（VLM）中解读并与复杂的现实环境互动。然而，这些方法仍受限于模仿学习的固有缺陷，即在训练过程中难以有效编码物理规则。现有方法通常依赖于复杂的基于规则的后处理优化，采用仍主要局限于模拟的强化学习，或使用需要大量计算资源的扩散引导。为解决这些挑战，我们引入了ReflectDrive，一种新颖的学习框架，通过离散扩散整合了反射机制以生成安全轨迹。我们首先将二维驾驶空间离散化以构建动作代码本，从而能够通过微调使用预训练的扩散语言模型进行规划任务。我们方法的核心是一个安全感知的反射机制，它无需梯度计算即可进行迭代自我校正。我们的方法从目标条件轨迹生成开始，以建模多模态驾驶行为。在此基础上，我们应用局部搜索方法识别不安全标记并确定可行解决方案，这些解决方案随后作为基于修复的再生成的安全锚点。在NAVSIM基准测试中评估，ReflectDrive在安全关键轨迹生成方面展示了显著优势，为自动驾驶系统提供了一个可扩展且可靠的解决方案。

English

End-to-End (E2E) solutions have emerged as a mainstream approach for autonomous driving systems, with Vision-Language-Action (VLA) models representing a new paradigm that leverages pre-trained multimodal knowledge from Vision-Language Models (VLMs) to interpret and interact with complex real-world environments. However, these methods remain constrained by the limitations of imitation learning, which struggles to inherently encode physical rules during training. Existing approaches often rely on complex rule-based post-refinement, employ reinforcement learning that remains largely limited to simulation, or utilize diffusion guidance that requires computationally expensive gradient calculations. To address these challenges, we introduce ReflectDrive, a novel learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion. We first discretize the two-dimensional driving space to construct an action codebook, enabling the use of pre-trained Diffusion Language Models for planning tasks through fine-tuning. Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient computation. Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors. Based on this, we apply local search methods to identify unsafe tokens and determine feasible solutions, which then serve as safe anchors for inpainting-based regeneration. Evaluated on the NAVSIM benchmark, ReflectDrive demonstrates significant advantages in safety-critical trajectory generation, offering a scalable and reliable solution for autonomous driving systems.

離散擴散在自動駕駛中的反射式視覺-語言-動作模型應用

Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving

摘要

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