标题： SEAL：基于视觉-语言模型的安全端到端协同自动驾驶自适应长尾建模 显示英文标题

标题： SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling

摘要： 自动驾驶技术在面对罕见、多样且视觉退化的天气场景时面临重大的安全挑战。 这些挑战在协作环境中变得更加关键，其中车辆和基础设施共同感知并推理复杂环境。 为了解决这些问题，我们提出了SEAL，这是一个基于视觉-语言模型的框架，采用自适应多模态学习，在长尾场景下实现稳健的协作自动驾驶。 SEAL引入了三项核心创新：(i) 一种提示驱动的长尾场景生成和评估管道，利用基础模型合成车辆和基础设施视角下的真实长尾条件，如雪和雾，高效丰富训练多样性；(ii) 一种门控多场景自适应注意力模块，利用场景先验调节视觉流，重新校准模糊或损坏的特征；以及(iii) 一种多任务场景感知对比学习目标，提高了多模态对齐并促进了跨场景特征可分性。 大量实验表明， 在复杂、具有挑战性的驾驶条件下，SEAL在推理、安全性和规划准确性方面显著优于现有基线，推动了自动驾驶的安全性、鲁棒性和可扩展性。 显示英文摘要

摘要： Autonomous driving technologies face significant safety challenges while operating under rare, diverse, and visually degraded weather scenarios. These challenges become more critical in cooperative settings, where vehicles and infrastructure jointly perceive and reason across complex environments. To address these issues, we propose SEAL, a vision-language model-based framework with adaptive multimodal learning for robust cooperative autonomous driving under long-tail scenarios. SEAL introduces three core innovations: (i) a prompt-driven long-tail scenario generation and evaluation pipeline that leverages foundation models to synthesize realistic long-tail conditions such as snow and fog across vehicle- and infrastructure-side views, enriching training diversity efficiently; (ii) a gated multi-scenario adaptive attention module that modulates the visual stream using scenario priors to recalibrate ambiguous or corrupted features; and (iii) a multi-task scenario-aware contrastive learning objective that improves multimodal alignment and promotes cross-scenario feature separability. Extensive experiments demonstrate that SEAL significantly outperforms existing baselines in reasoning, safety, and planning accuracy under complex, challenging driving conditions, advancing the safety, robustness, and scalability of autonomous driving.