SUPER-AD：语义不确定性感知的端到端鲁棒自动驾驶规划
SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving
AI总结：
SUPER-AD通过语义不确定性感知和驾驶先验知识，提升端到端自动驾驶在复杂不确定性条件下的安全性和可靠性。
摘要：
端到端（E2E）规划已成为自动驾驶领域强大的范式，但当前系统仍然本质上缺乏不确定性感知。它们假设感知输出是完全可靠的，即使在模糊或观察不足的场景中，使规划器无法获得不确定性量化的明确度量。为了解决这一限制，我们提出了一种仅依赖摄像头的E2E框架，直接在鸟瞰图（BEV）空间中估计随机不确定性，并将其纳入规划。我们的方法生成了一张密集的、具有不确定性的可驾驶性地图，能够在像素级分辨率下捕捉语义结构和几何布局。为进一步促进安全且符合规则的行为，我们引入了车道跟随正则化，编码车道结构和交通规范。这一先验条件在正常条件下稳定轨迹规划，同时保留了进行超车或变道等操作所需的灵活性。共同，这些组件使在挑战性不确定性条件下能够实现稳健且可解释的轨迹规划。在NAVSIM基准测试中，我们的方法实现了最先进的性能，在具有挑战性的NAVHARD和NAVSAFE子集上取得了显著提升。这些结果表明，我们的系统性随机不确定性建模结合驾驶先验知识显著推动了仅依赖摄像头的端到端自动驾驶的安全性和可靠性。
英文摘要：
End-to-End (E2E) planning has become a powerful paradigm for autonomous driving, yet current systems remain fundamentally uncertainty-blind. They assume perception outputs are fully reliable, even in ambiguous or poorly observed scenes, leaving the planner without an explicit measure of uncertainty. To address this limitation, we propose a camera-only E2E framework that estimates aleatoric uncertainty directly in BEV space and incorporates it into planning. Our method produces a dense, uncertainty-aware drivability map that captures both semantic structure and geometric layout at pixel-level resolution. To further promote safe and rule-compliant behavior, we introduce a lane-following regularization that encodes lane structure and traffic norms. This prior stabilizes trajectory planning under normal conditions while preserving the flexibility needed for maneuvers such as overtaking or lane changes. Together, these components enable robust and interpretable trajectory planning, even under challenging uncertainty conditions. Evaluated on the NAVSIM benchmark, our method achieves state-of-the-art performance, delivering substantial gains on both the challenging NAVHARD and NAVSAFE subsets. These results demonstrate that our principled aleatoric uncertainty modeling combined with driving priors significantly advances the safety and reliability of camera-only E2E autonomous driving.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
