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

Robust perception is a vital component for ensuring safe autonomous andassisted driving. Automotive radar (77 to 81 GHz), which offersweather-resilient sensing, provides a complementary capability to the vision-or LiDAR-based autonomous driving systems. Raw radio-frequency (RF) radartensors contain rich spatiotemporal semantics besides 3D location information.The majority of previous methods take in 3D (Doppler-range-azimuth) RF radartensors, allowing prediction of an object's location, heading angle, and sizein bird's-eye-view (BEV). However, they lack the ability to at the same timeinfer objects' size, orientation, and identity in the 3D space. To overcomethis limitation, we propose an efficient joint architecture calledCenterRadarNet, designed to facilitate high-resolution representation learningfrom 4D (Doppler-range-azimuth-elevation) radar data for 3D object detectionand re-identification (re-ID) tasks. As a single-stage 3D object detector,CenterRadarNet directly infers the BEV object distribution confidence maps,corresponding 3D bounding box attributes, and appearance embedding for eachpixel. Moreover, we build an online tracker utilizing the learned appearanceembedding for re-ID. CenterRadarNet achieves the state-of-the-art result on theK-Radar 3D object detection benchmark. In addition, we present the first 3Dobject-tracking result using radar on the K-Radar dataset V2. In diversedriving scenarios, CenterRadarNet shows consistent, robust performance,emphasizing its wide applicability.

CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar