Data annotation in autonomous vehicles is a critical step in the development of Deep Neural Network (DNN) based models or the performance evaluation of the perception system. This often takes the form of adding 3D bounding boxes on time-sequential and registered series of point-sets captured from active sensors like Light Detection and Ranging (LiDAR) and Radio Detection and Ranging (RADAR). When annotating multiple active sensors, there is a need to motion compensate and translate the points to a consistent coordinate frame and timestamp respectively. However, highly dynamic objects pose a unique challenge, as they can appear at different timestamps in each sensor's data. Without knowing the speed of the objects, their position appears to be different in different sensor outputs. Thus, even after motion compensation, highly dynamic objects are not matched from multiple sensors in the same frame, and human annotators struggle to add unique bounding boxes that capture all objects. This article focuses on addressing this challenge, primarily within the context of Scania collected datasets. The proposed solution takes a track of an annotated object as input and uses the Moving Horizon Estimation (MHE) to robustly estimate its speed. The estimated speed profile is utilized to correct the position of the annotated box and add boxes to object clusters missed by the original annotation.

翻译：自动驾驶车辆中的数据标注是基于深度神经网络模型开发或感知系统性能评估的关键步骤。该过程通常表现为在时间序列且配准的点集序列中添加三维边界框，这些点集由激光雷达（LiDAR）和雷达（RADAR）等主动传感器采集。当标注多个主动传感器数据时，需对点云进行运动补偿并将其分别转换至统一坐标系与时间戳。然而，高动态目标带来了独特挑战：同一目标在不同传感器数据中可能出现在不同时间戳。在未知目标速度的情况下，其位置在不同传感器输出中存在差异。因此，即使经过运动补偿，多传感器高动态目标也无法在同一帧中匹配，标注人员难以添加唯一性边界框以捕获全部目标。本文聚焦于解决该挑战，主要基于斯堪尼亚采集的数据集展开研究。所提出的方案以已标注目标的轨迹为输入，采用移动时域估计（MHE）方法鲁棒估算其速度，并利用估算速度曲线修正标注框位置，同时为原始标注遗漏的目标簇补充标注框。