标题： ReAL-AD：迈向端到端自动驾驶中类人推理 显示英文标题

标题： ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving

摘要： 端到端的自动驾驶已成为一种有前景的方法，它在单一框架内统一感知、预测和规划，减少信息丢失并提高适应性。 然而，现有方法通常依赖于固定且稀疏的轨迹监督，限制了它们捕捉人类驾驶员自然使用的层次推理过程的能力。 为了弥补这一差距，我们提出了ReAL-AD，这是一种增强推理的学习框架，基于三层人类认知模型：驾驶策略、驾驶决策和驾驶操作，其中引入了视觉语言模型（VLMs）以增强这些层级之间的情境意识和结构化推理。 具体来说，我们引入了：(1) 战略推理注入器，通过解释VLM生成的见解来制定高层次的驾驶策略；(2) 战术推理整合器，将战略意图细化为可解释的战术选择，如变道、超车和速度调整；(3) 层次轨迹解码器，逐步将战术决策转化为精确的控制动作，以实现平滑且类人的轨迹执行。 广泛的评估表明，集成我们的框架使规划准确性和安全性提高了30%以上，使端到端自动驾驶更具可解释性，并与类人的层次推理一致。 项目页面可在以下地址找到： \href{https://4dvlab.github.io/project_page/realad}{\texttt{4dvlab.github.io/project_page/realad}} 显示英文摘要

摘要： End-to-end autonomous driving has emerged as a promising approach to unify perception, prediction, and planning within a single framework, reducing information loss and improving adaptability. However, existing methods often rely on fixed and sparse trajectory supervision, limiting their ability to capture the hierarchical reasoning process that human drivers naturally employ. To bridge this gap, we propose ReAL-AD, a Reasoning-Augmented Learning framework that structures decision-making in autonomous driving based on the three-tier human cognitive model: Driving Strategy, Driving Decision, and Driving Operation, where Vision-Language Models (VLMs) are incorporated to enhance situational awareness and structured reasoning across these levels. Specifically, we introduce: (1) the Strategic Reasoning Injector, which formulates high-level driving strategies by interpreting complex traffic contexts from VLM-generated insights; (2) the Tactical Reasoning Integrator, which refines strategic intent into interpretable tactical choices such as lane changes, overtaking, and speed adjustments; and (3) the Hierarchical Trajectory Decoder, which progressively translates tactical decisions into precise control actions for smooth and human-like trajectory execution. Extensive evaluations show that integrating our framework improves planning accuracy and safety by over 30%, making end-to-end autonomous driving more interpretable and aligned with human-like hierarchical reasoning. The project page can be found at: \href{https://4dvlab.github.io/project_page/realad}{\texttt{4dvlab.github.io/project\_page/realad}}