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

Predicting the states of dynamic traffic actors into the future is importantfor autonomous systems to operate safelyand efficiently. Remarkably, the mostcritical scenarios aremuch less frequent and more complex than theuncriticalones. Therefore, uncritical cases dominate the prediction. In thispaper, we address specifically the challenging scenarios at the long tail ofthe dataset distribution. Our analysis shows that the common losses tend toplace challenging cases suboptimally in the embedding space. As a consequence,we propose to supplement the usual loss with aloss that places challengingcases closer to each other. This triggers sharing information among challengingcases andlearning specific predictive features. We show on four public datasetsthat this leads to improved performance on the challenging scenarios while theoverall performance stays stable. The approach is agnostic w.r.t. the usednetwork architecture, input modality or viewpoint, and can be integrated intoexisting solutions easily. Code is available athttps://github.com/lmb-freiburg/Contrastive-Future-Trajectory-Prediction

On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors