In　vicw　of　thc　control　architecture　and　driving　authority　decision-making,　the　research　Status　and　developmcnt　trcnd　of　driver-automation　cooperative　driving　were　expounded.　In　terms　of　control　architecture,　the　characteristics　and　application　ränge　of　switching　control　architecture　and　shared　control　architecture　were　analyzed,　and　the　coneept　of　hybrid　control　architecture　was　proposed.　In　terms　of　driving　authority　decision-making,　the　ways　of　using　different　sources　and　natures　of　information　in　different　driving　authority　decision-making　methods　were　discussed.　The　methods　involved　in　the　direet　and　indirect　shared　control　methods　when　implcmenting　the　allocation　of　driving　authority　were　summarized.　Thc　research　perspectives　and　methods　of　decision-making　at　the　strategy　level　and　the　executive　level　were　sorted　out.　Research　results　show　that　for　the　safety　problcms　of　high-lcvcl　automated　driving　on　the　road,　the　development　of　hybrid　control　architecturc　for　dcscribing　the　System　dynamics　under　human　safety　Intervention　scenarios　is　condueive　to　avoiding　model　mismatch,　which　provides　the　foundation　for　control　Performance　optimization　and　stability　design.　By　integrating　holographic　situational　awareness　and　data　intelligence　to　collect　and　integrate　data　from　multiple　Information　sources,　the　dynamic　changes　of　many　factors　in　the　driver-automation　cooperative　driving　System　can　be　morc　comprehensively　understood,　and　the　optimal　driving　authority　decision　can　be　made.　Compared　with　direct　shared　control,　indireet　shared　control　can　avoid　direct　confrontation　between　driver　and　automation　control　flows.　However,　at　the　executive　level　of　dynamic　driving　authority　allocation,　it　is　necessary　to　consider　the　conflict　feedback　between　driver　and　automation　and　ensure　a　reasonablc　intcractive　experience,　so　as　to　reflect　the　advantages　of　indireet　shared　control.　The　decision-making　method　based　on　the　agent　at　the　strategy　level　is　independent　of　the　aecuraey　of　the　mathematical　model　and　can　adapt　to　the　dynamic　change　of　the　environment.　The　decision-making　method　based　on　game　theory　at　the　executive　level　can　enhance　the　controllability　and　explainability　of　the　driving　authority　decision-making　System　by　modeling　the　driver-automation　interaction　process.　In　the　future,　the　driver-automation　cooperative　driving　System　should　be　designed　to　further　optimize　the　interactive　experienec.　Mcanwhile,　the　development　of　equal　and　inclusive　driver-automation　rclationships　is　necessary.　The　robustness　of　the　control　System　and　the　interpretation　and　adaptability　of　driving　authority　decision-making　should　be　improved　as　well.　©　2025　Chang＇an　University.　All　rights　reserved.