标题： DriveMind：一种基于双视觉语言模型的自动驾驶强化学习框架 显示英文标题

标题： DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving

摘要： 端到端的自动驾驶系统直接将传感器数据映射到控制命令，但这些系统仍然缺乏透明性、可解释性和正式的安全保障。 尽管最近的视觉-语言引导的强化学习（RL）方法引入了语义反馈，但它们往往依赖静态提示和固定目标，限制了对动态驾驶场景的适应能力。 我们提出了 DriveMind，这是一个统一的语义奖励框架，集成了以下组件：(i) 对于逐步语义锚定的对比视觉-语言模型（VLM）编码器；(ii) 基于新颖性触发的 VLM 编码器-解码器，通过链式思维（CoT）蒸馏微调，用于在语义漂移时生成动态提示；(iii) 一个分层安全模块，强制执行运动学约束（如速度、车道居中、稳定性）；以及 (iv) 一个紧凑的预测世界模型，以实现与预期理想状态的奖励对齐。 DriveMind 在 CARLA Town 2 中实现了平均速度 19.4 ± 2.3 km/h、路线完成率 0.98 ± 0.03，并且几乎零碰撞，成功率为基线高出 4% 以上。 其语义奖励在最小分布偏移的情况下能够零样本推广到真实的车载摄像头数据，展示了跨领域的稳健对齐和实际部署潜力。 显示英文摘要

摘要： End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vision-language-guided reinforcement learning (RL) methods introduce semantic feedback, they often rely on static prompts and fixed objectives, limiting adaptability to dynamic driving scenes. We present DriveMind, a unified semantic reward framework that integrates: (i) a contrastive Vision-Language Model (VLM) encoder for stepwise semantic anchoring; (ii) a novelty-triggered VLM encoder-decoder, fine-tuned via chain-of-thought (CoT) distillation, for dynamic prompt generation upon semantic drift; (iii) a hierarchical safety module enforcing kinematic constraints (e.g., speed, lane centering, stability); and (iv) a compact predictive world model to reward alignment with anticipated ideal states. DriveMind achieves 19.4 +/- 2.3 km/h average speed, 0.98 +/- 0.03 route completion, and near-zero collisions in CARLA Town 2, outperforming baselines by over 4% in success rate. Its semantic reward generalizes zero-shot to real dash-cam data with minimal distributional shift, demonstrating robust cross-domain alignment and potential for real-world deployment.