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

Current end-to-end deep learning driving models have two problems: (1) Poorgeneralization ability of unobserved driving environment when diversity oftraining driving dataset is limited (2) Lack of accident explanation abilitywhen driving models don't work as expected. To tackle these two problems,rooted on the believe that knowledge of associated easy task is benificial foraddressing difficult task, we proposed a new driving model which is composed ofperception module for \textit{see and think} and driving module for\textit{behave}, and trained it with multi-task perception-related basicknowledge and driving knowledge stepwisely.  Specifically segmentation map and depth map (pixel level understanding ofimages) were considered as \textit{what \& where} and \textit{how far}knowledge for tackling easier driving-related perception problems beforegenerating final control commands for difficult driving task. The results ofexperiments demonstrated the effectiveness of multi-task perception knowledgefor better generalization and accident explanation ability. With our method theaverage sucess rate of finishing most difficult navigation tasks in untrainedcity of CoRL test surpassed current benchmark method for 15 percent in trainedweather and 20 percent in untrained weathers. Demonstration video link is:https://www.youtube.com/watch?v=N7ePnnZZwdE

Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability