R4D: Utilizing Reference Objects for Long-Range Distance Estimation
This addresses a safety-critical gap in autonomous driving by focusing on long-range objects, which are neglected in existing methods, though it is incremental as it builds on prior distance estimation techniques.
The paper tackles the problem of long-range distance estimation for autonomous driving by introducing a new task and datasets, and proposes R4D, a framework that uses reference objects with known distances to improve accuracy, showing significant improvements over baselines.
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.