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

We consider a mixed autonomy scenario where the traffic intersectioncontroller decides whether the traffic light will be green or red at each lanefor multiple traffic-light blocks. The objective of the traffic intersectioncontroller is to minimize the queue length at each lane and maximize theoutflow of vehicles over each block. We consider that the traffic intersectioncontroller informs the autonomous vehicle (AV) whether the traffic light willbe green or red for the future traffic-light block. Thus, the AV can adapt itsdynamics by solving an optimal control problem. We model the decision processof the traffic intersection controller as a deterministic delay Markov decisionprocess owing to the delayed action by the traffic controller. We proposeReinforcement-learning based algorithm to obtain the optimal policy. We show -empirically - that our algorithm converges and reduces the energy costs of AVsdrastically as the traffic controller communicates with the AVs.

Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach