Abstract
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.
Abstract (translated)
端到端（E2E）解决方案已成为自主驾驶系统中的主流方法，其中视觉-语言-行动（VLA）模型代表了一种新的范式，该范式利用了从视觉-语言模型（VLMs）中预训练的多模态知识来解释和与复杂的现实世界环境互动。然而，这些方法仍然受限于模仿学习的局限性，在培训过程中难以内在地编码物理规则。现有方法往往依赖于复杂基于规则的事后优化、主要限于模拟中的强化学习或需要计算成本高昂的梯度计算的扩散引导。为了解决这些问题，我们引入了ReflectDrive，这是一种新的学习框架，通过离散扩散整合了一个反射机制以生成安全轨迹。首先，我们将二维驾驶空间离散化来构建一个动作代码本，从而能够通过微调预训练的扩散语言模型来进行规划任务。我们的方法的核心是一个具有安全意识的反思机制，该机制可以在没有梯度计算的情况下进行迭代自我修正。我们的方法从基于目标条件的轨迹生成开始建模多模式驾驶行为，并在此基础上应用局部搜索方法来识别不安全令牌并确定可行方案，这些方案随后作为再生中的安全锚点使用。在NAVSIM基准上评估后，ReflectDrive展示了在关键安全性轨迹生成方面的显著优势，为自主驾驶系统提供了可扩展且可靠的解决方案。
URL
https://arxiv.org/abs/2509.20109
