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 change in dense traffic is considered a challenging problem thattypically requires the recognition of an opportune and appropriate opportunityfor maneuvers. In this work, we propose a chance-aware lane-change strategywith high-level model predictive control (MPC) through curriculum reinforcementlearning (CRL). The embodied MPC in our framework is parameterized withaugmented decision variables, where full-state references and regulatoryfactors concerning their relative importance are introduced. Furthermore, toimprove the convergence speed and ensure a high-quality policy, effectivecurriculum design is integrated into the reinforcement learning (RL) frameworkwith policy transfer and enhancement. Then the proposed framework is deployedto numerical simulations towards dense and dynamic traffic. It is noteworthythat, given a narrow chance, the proposed approach generates high-qualitylane-change maneuvers such that the vehicle merges into the traffic flow with ahigh success rate of 96%.

Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning