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

Highway merging scenarios featuring mixed traffic conditions pose significantmodeling and control challenges for connected and automated vehicles (CAVs)interacting with incoming on-ramp human-driven vehicles (HDVs). In this paper,we present an approach to learn an approximate information state model ofCAV-HDV interactions for a CAV to maneuver safely during highway merging. Inour approach, the CAV learns the behavior of an incoming HDV using approximateinformation states before generating a control strategy to facilitate merging.First, we validate the efficacy of this framework on real-world data by usingit to predict the behavior of an HDV in mixed traffic situations extracted fromthe Next-Generation Simulation repository. Then, we generate simulation datafor HDV-CAV interactions in a highway merging scenario using a standard inversereinforcement learning approach. Without assuming a prior knowledge of thegenerating model, we show that our approximate information state model learnsto predict the future trajectory of the HDV using only observations.Subsequently, we generate safe control policies for a CAV while merging withHDVs, demonstrating a spectrum of driving behaviors, from aggressive toconservative. We demonstrate the effectiveness of the proposed approach byperforming numerical simulations.

Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging