注意铰接处：面向自动驾驶卡车的动态标定与铰接感知
Mind the Hitch: Dynamic Calibration and Articulated Perception for Autonomous Trucks
把铰接式卡车的车头-挂车相机外参从静态标定改为用跨视角+时序注意力在线回归 6-DoF 相对位姿，并直接替换 BEVFormer 的静态外参，为多相机 BEV 感知在基座随时间变化的场景（挂车形变、遮挡、快速折角）提供了可复用的动态标定模块与 CARLA 基准。
原文依据 dCAP employs a transformer with cross-view and temporal attention to robustly aggregate spatial cues while maintaining temporal consistency
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自动驾驶卡车由于牵引车-挂车的铰接几何结构，以及第五轮关节和挂车形变引起的传感器位姿时变，带来了独特挑战。现有感知与标定方法假设基线静止，或依赖大视差与纹理丰富的场景，限制了其在真实环境下的可靠性。我们提出 dCAP（动态标定与铰接感知），一个基于视觉的框架，持续估计牵引车相机与挂车相机之间的 6 自由度相对位姿。dCAP 采用带有跨视角注意力与时序注意力的 Transformer，在保持时序一致性的同时稳健地聚合空间线索，从而在快速铰接与遮挡条件下实现准确感知。与 BEVFormer 集成后，dCAP 以动态预测的外参替代静态标定，提升了 3D 目标检测性能。为便于评测，我们提出 STT4AT，一个基于 CARLA 的基准，模拟半挂卡车，配备同步多传感器套件，并在多样环境中具有时变的传感器组间几何关系。实验表明，dCAP 实现了稳定、准确的感知，同时解决了静态标定在自动驾驶卡车中的局限。数据集、开发工具包与源代码将公开发布。
英文原文摘要 1310 字符
Autonomous trucking poses unique challenges due to articulated tractor-trailer geometry, and time-varying sensor poses caused by the fifth-wheel joint and trailer flex. Existing perception and calibration methods assume static baselines or rely on high-parallax and texture-rich scenes, limiting their reliability under real-world settings. We propose dCAP (dynamic Calibration and Articulated Perception), a vision-based framework that continuously estimates the 6-DoF (degree of freedom) relative pose between tractor and trailer cameras. dCAP employs a transformer with cross-view and temporal attention to robustly aggregate spatial cues while maintaining temporal consistency, enabling accurate perception under rapid articulation and occlusion. Integrated with BEVFormer, dCAP improves 3D object detection by replacing static calibration with dynamically predicted extrinsics. To facilitate evaluation, we introduce STT4AT, a CARLA-based benchmark simulating semi-trailer trucks with synchronized multi-sensor suites and time-varying inter-rig geometry across diverse environments. Experiments demonstrate that dCAP achieves stable, accurate perception while addressing the limitations of static calibration in autonomous trucking. The dataset, development kit, and source code will be publicly released.