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

The prediction of road users' future motion is a critical task in supportingadvanced driver-assistance systems (ADAS). It plays an even more crucial rolefor autonomous driving (AD) in enabling the planning and execution of safedriving maneuvers. Based on our previous work, Context-Aware Scene PredictionNetwork (CASPNet), an improved system, CASPNet++, is proposed. In this work, wefocus on further enhancing the interaction modeling and scene understanding tosupport the joint prediction of all road users in a scene using spatiotemporalgrids to model future occupancy. Moreover, an instance-based output head isintroduced to provide multi-modal trajectories for agents of interest. Inextensive quantitative and qualitative analysis, we demonstrate the scalabilityof CASPNet++ in utilizing and fusing diverse environmental input sources suchas HD maps, Radar detection, and Lidar segmentation. Tested on theurban-focused prediction dataset nuScenes, CASPNet++ reaches state-of-the-artperformance. The model has been deployed in a testing vehicle, running inreal-time with moderate computational resources.

CASPNet++: Joint Multi-Agent Motion Prediction