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

In this paper, we develop an optimal weight adaptation strategy of modelpredictive control (MPC) for connected and automated vehicles (CAVs) in mixedtraffic. We model the interaction between a CAV and a human-driven vehicle(HDV) as a simultaneous game and formulate a game-theoretic MPC problem to finda Nash equilibrium of the game. In the MPC problem, the weights in the HDV'sobjective function can be learned online using moving horizon inversereinforcement learning. Using Bayesian optimization, we propose a strategy tooptimally adapt the weights in the CAV's objective function so that theexpected true cost when using MPC in simulations can be minimized. We validatethe effectiveness of the optimal strategy by numerical simulations of a vehiclecrossing example at an unsignalized intersection.

Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization