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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In autonomous driving, goal-based multi-trajectory prediction methods areproved to be effective recently, where they first score goal candidates, thenselect a final set of goals, and finally complete trajectories based on theselected goals. However, these methods usually involve goal predictions basedon sparse predefined anchors. In this work, we propose an anchor-free model,named DenseTNT, which performs dense goal probability estimation for trajectoryprediction. Our model achieves state-of-the-art performance, and ranks 1st onthe Waymo Open Dataset Motion Prediction Challenge. Project page is athttps://github.com/Tsinghua-MARS-Lab/DenseTNT.

DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution