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

Lane detection is a vital task for vehicles to navigate and localize theirposition on the road. To ensure reliable results, lane detection algorithmsmust have robust generalization performance in various road environments.However, despite the significant performance improvement of deep learning-basedlane detection algorithms, their generalization performance in response tochanges in road environments still falls short of expectations. In this paper,we present a novel framework for single-source domain generalization (SSDG) inlane detection. By decomposing data into lane structures and surroundings, weenhance diversity using High-Definition (HD) maps and generative models. Ratherthan expanding data volume, we strategically select a core subset of data,maximizing diversity and optimizing performance. Our extensive experimentsdemonstrate that our framework enhances the generalization performance of lanedetection, comparable to the domain adaptation-based method.

Improving Lane Detection Generalization: A Novel Framework using HD Maps for Boosting Diversity