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

Connected and automated vehicles (CAVs) are viewed as a special kind ofrobots that have the potential to significantly improve the safety andefficiency of traffic. In contrast to many swarm robotics studies that aredemonstrated in labs by employing a small number of robots, CAV studies aims toachieve cooperative driving of unceasing robot swarm flows. However, how to getthe optimal passing order of such robot swarm flows even for a signal-freeintersection is an NP-hard problem (specifically, enumerating based algorithmtakes days to find the optimal solution to a 20-CAV scenario). Here, weintroduce a novel cooperative driving algorithm (AlphaOrder) that combinesoffline deep learning and online tree searching to find a near-optimal passingorder in real-time. AlphaOrder builds a pointer network model from solvedscenarios and generates near-optimal passing orders instantaneously for newscenarios. Furthermore, our approach provides a general approach to managingpreemptive resource sharing between swarm robotics (e.g., scheduling multipleautomated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) atconflicting areas

Coordinating CAV Swarms at Intersections with a Deep Learning Model