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

Environment perception including detection, classification, tracking, andmotion prediction are key enablers for automated driving systems andintelligent transportation applications. Fueled by the advances in sensingtechnologies and machine learning techniques, LiDAR-based sensing systems havebecome a promising solution. The current challenges of this solution are how toeffectively combine different perception tasks into a single backbone and howto efficiently learn the spatiotemporal features directly from point cloudsequences. In this research, we propose a novel spatiotemporal attentionnetwork based on a transformer self-attention mechanism for joint semanticsegmentation and motion prediction within a point cloud at the voxel level. Thenetwork is trained to simultaneously outputs the voxel level class andpredicted motion by learning directly from a sequence of point cloud datasets.The proposed backbone includes both a temporal attention module (TAM) and aspatial attention module (SAM) to learn and extract the complex spatiotemporalfeatures. This approach has been evaluated with the nuScenes dataset, andpromising performance has been achieved.

Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud