The　concept　of　shared　control　has　garnered　significant　attention　within　the　realm　of　human-machine　hybrid　intelligence　research.　This　study　introduces　a　novel　approach,　specifically　a　dynamic　control　authority　allocation　method,　for　implementing　shared　control　in　autonomous　vehicles.　Unlike　conventional　mixed-initiative　control　techniques　that　blend　human　and　vehicle　inputs　with　weights　determined　by　predefined　index,　the　proposed　method　utilizes　optimization-based　techniques　to　obtain　an　optimal　dynamic　allocation　for　human　and　vehicle　inputs　that　satisfies　safety　constraints.　Specifically,　a　convex　quadratic　programm　(QP)　is　constructed　incorporating　control　barrier　functions　(CBF)　for　safety　and　control　Lyapunov　functions　(CLF)　for　satisfying　automated　control　objectives.　The　cost　function　of　the　QP　is　designed　such　that　human　weight　increases　with　the　magnitude　of　human　input.　A　smooth　control　authority　transition　is　obtained　by　optimizing　over　the　change　rate　of　the　weight　instead　of　the　weight　itself.　The　proposed　method　is　verified　in　lane-changing　scenarios　with　human-in-the-loop　(HmIL)　and　hardware-in-the-loop　(HdIL)　experiments.　Results　show　that　the　proposed　method　outperforms　index-based　control　authority　allocation　method　in　terms　of　agility,　safety　and　comfort.　　©　2000-2011　IEEE.