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

For autonomous vehicles to proactively plan safe trajectories and makeinformed decisions, they must be able to predict the future occupancy states ofthe local environment. However, common issues with occupancy prediction includepredictions where moving objects vanish or become blurred, particularly atlonger time horizons. We propose an environment prediction framework thatincorporates environment semantics for future occupancy prediction. Our methodfirst semantically segments the environment and uses this information alongwith the occupancy information to predict the spatiotemporal evolution of theenvironment. We validate our approach on the real-world Waymo Open Dataset.Compared to baseline methods, our model has higher prediction accuracy and iscapable of maintaining moving object appearances in the predictions for longerprediction time horizons.

Predicting Future Spatiotemporal Occupancy Grids with Semantics for Autonomous Driving