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

While supervised learning is widely used for perception modules inconventional autonomous driving solutions, scalability is hindered by the hugeamount of data labeling needed. In contrast, while end-to-end architectures donot require labeled data and are potentially more scalable, interpretability issacrificed. We introduce a novel architecture that is trained in a fullyself-supervised fashion for simultaneous multi-step prediction of space-timecost map and road dynamics. Our solution replaces the manually designed costfunction for motion planning with a learned high dimensional cost map that isnaturally interpretable and allows diverse contextual information to beintegrated without manual data labeling. Experiments on real world driving datashow that our solution leads to lower number of collisions and road violationsin long planning horizons in comparison to baselines, demonstrating thefeasibility of fully self-supervised prediction without sacrificing eitherscalability or interpretability.

Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map