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

Intelligent Transportation System (ITS) has become one of the essentialcomponents in Industry 4.0. As one of the critical indicators of ITS,efficiency has attracted wide attention from researchers. However, the nextgeneration of urban traffic carried by multiple transport service providers mayprohibit the raw data interaction among multiple regions for privacy reasons,easily ignored in the existing research. This paper puts forward a federatedlearning-based vehicle control framework to solve the above problem, includinginteractors, trainers, and an aggregator. In addition, the density-aware modelaggregation method is utilized in this framework to improve vehicle control.What is more, to promote the performance of the end-to-end learning algorithmin the safety aspect, this paper proposes an imitation learning algorithm,which can obtain collision avoidance capabilities from a set of collisionavoidance rules. Furthermore, a loss-aware experience selection strategy isalso explored, reducing the communication overhead between the interactors andthe trainers via extra computing. Finally, the experiment results demonstratethat the proposed imitation learning algorithm obtains the ability to avoidcollisions and reduces discomfort by 55.71%. Besides, density-aware modelaggregation can further reduce discomfort by 41.37%, and the experienceselection scheme can reduce the communication overhead by 12.80% while ensuringmodel convergence.

Density-Aware Federated Imitation Learning for Connected and Automated Vehicles with Unsignalized Intersection