When　multiple　autonomous　vehicles　perform　lane　change　and　merging　tasks　on　structured　road，steering　and　merging　actions　need　to　be　comprehensively　considered　to　avoid　potential　accidents.　Meanwhile，the　changing　road　curvature　and　surrounding　vehicle　speed　also　increase　the　difficulty　of　cooperative　control.　Focusing　on　the　above　issues，this　paper　proposes　a　multi-vehicle　lane　change　gaming　motion　planning　and　cooperative　control　method　facing　variable　curvature　road.　Firstly，a　multi-vehicle　model　in　curvature　coordinate　system　is　developed　to　determine　the　inter-vehicle　safety　distance　and　dynamics　state.　Then，by　systematically　considering　the　road　curvature　variation　and　surrounding　vehicle　information，a　game-based　multi-vehicle　lane　change　motion　planning　algorithm　is　proposed，which　uses　a　distributed　framework　to　quickly　solve　the　optimal　speed　trajectory　and　lane　change　timing　considering　personalized　driving.　Finally，the　road　curvature　and　planning　trajectory　are　identified　effectively　based　on　B-sample　curve，and　an　adaptive　time-varying　model　predictive　control　algorithm　is　constructed　to　achieve　trajectory　tracking.　Specifically，the　control　parameters　are　updated　in　real　time　under　the　single-step　prediction　domain　to　eliminate　the　control　deviations　caused　by　frequently　various　vehicle　speed　and　curvature.　The　co-simulation　results　show　that　the　proposed　method　can　reduce　the　tracking　error　by　58%　compared　to　the　Stanley　method，with　reduction　of　the　merging　time　by　74%　compared　to　the　cooperative　adaptive　cruise　control　method.　Moreover，the　computational　solution　efficiency　is　only　10%　of　the　centralized　method.　©　2023　SAE-China.　All　rights　reserved.