Estimating the distance of objects is a safety-critical task for autonomous
driving. Focusing on short-range objects, existing methods and datasets neglect
the equally important long-range objects. In this paper, we introduce a
challenging and under-explored task, which we refer to as Long-Range Distance
Estimation, as well as two datasets to validate new methods developed for this
task. We then proposeR4D, the first framework to accurately estimate the
distance of long-range objects by using references with known distances in the
scene. Drawing inspiration from human perception, R4D builds a graph by
connecting a target object to all references. An edge in the graph encodes the
relative distance information between a pair of target and reference objects.
An attention module is then used to weigh the importance of reference objects
and combine them into one target object distance prediction. Experiments on the
two proposed datasets demonstrate the effectiveness and robustness of R4D by
showing significant improvements compared to existing baselines. We are looking
to make the proposed dataset, Waymo OpenDataset - Long-Range Labels, available
publicly at waymo.com/open/download.

本文首次提出了一个重要而未被充分研究的任务 -- 长距离目标距离估计，介绍了两个数据集，并提出了一种称为 R4D 的算法框架，使用已知距离的参考对象来准确估计长距离物体的距离。该算法基于人类认知的模式建造了目标到参考对象之间的图，并利用注意力模块自适应地组合参考对象，实现了目标距离的预测。实验证明，R4D 与已有算法相比具有更好的效果和鲁棒性。