English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Estimating the distance of objects is a safety-critical task for autonomousdriving. Focusing on short-range objects, existing methods and datasets neglectthe equally important long-range objects. In this paper, we introduce achallenging and under-explored task, which we refer to as Long-Range DistanceEstimation, as well as two datasets to validate new methods developed for thistask. We then proposeR4D, the first framework to accurately estimate thedistance of long-range objects by using references with known distances in thescene. Drawing inspiration from human perception, R4D builds a graph byconnecting a target object to all references. An edge in the graph encodes therelative distance information between a pair of target and reference objects.An attention module is then used to weigh the importance of reference objectsand combine them into one target object distance prediction. Experiments on thetwo proposed datasets demonstrate the effectiveness and robustness of R4D byshowing significant improvements compared to existing baselines. We are lookingto make the proposed dataset, Waymo OpenDataset - Long-Range Labels, availablepublicly at waymo.com/open/download.

R4D: Utilizing Reference Objects for Long-Range Distance Estimation