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/

Zendy Plus

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

Zendy Tools

Zendy Open

This work aims to address the challenges in autonomous driving by focusing onthe 3D perception of the environment using roadside LiDARs. We design a 3Dobject detection model that can detect traffic participants in roadside LiDARsin real-time. Our model uses an existing 3D detector as a baseline and improvesits accuracy. To prove the effectiveness of our proposed modules, we train andevaluate the model on three different vehicle and infrastructure datasets. Toshow the domain adaptation ability of our detector, we train it on aninfrastructure dataset from China and perform transfer learning on a differentdataset recorded in Germany. We do several sets of experiments and ablationstudies for each module in the detector that show that our model outperformsthe baseline by a significant margin, while the inference speed is at 45 Hz (22ms). We make a significant contribution with our LiDAR-based 3D detector thatcan be used for smart city applications to provide connected and automatedvehicles with a far-reaching view. Vehicles that are connected to the roadsidesensors can get information about other vehicles around the corner to improvetheir path and maneuver planning and to increase road traffic safety.

Real-Time And Robust 3D Object Detection with Roadside LiDARs