论文标题
系统驱动的自动地面真相生成方法，用于DL市中心驾驶走廊探测器
A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors
论文作者
论文摘要
数据驱动的感知方法在自动驾驶系统中建立了良好的。在许多领域，甚至达到了超人的表现。与预测和计划方法不同，主要是监督的学习算法用于感知领域。因此，剩下的主要挑战是有效地生成地面真相数据。由于感知模块位于传感器附近，因此它们通常在高带宽的原始传感器数据上运行。因此，地面真相标签的产生通常会引起大量的手动努力，这为标签本身和必要的质量控制带来了高昂的成本。在这一贡献中，我们提出了一种自动标记方法，用于对可驱动的自我走廊的语义分割，该方法将手动努力减少了150倍以上。所提出的整体方法可以在自动数据循环中使用，从而可以持续改进根据感知模块。
Data-driven perception approaches are well-established in automated driving systems. In many fields even super-human performance is reached. Unlike prediction and planning approaches, mainly supervised learning algorithms are used for the perception domain. Therefore, a major remaining challenge is the efficient generation of ground truth data. As perception modules are positioned close to the sensor, they typically run on raw sensor data of high bandwidth. Due to that, the generation of ground truth labels typically causes a significant manual effort, which leads to high costs for the labelling itself and the necessary quality control. In this contribution, we propose an automatic labeling approach for semantic segmentation of the drivable ego corridor that reduces the manual effort by a factor of 150 and more. The proposed holistic approach could be used in an automated data loop, allowing a continuous improvement of the depending perception modules.
