To　further　enhance　the　role　of　connected　vehicles　with　the　Human-Machine　Interface　(HMI)　in　helping　drivers　in　foggy　weather,　it　is　meaningful　to　optimize　the　HMI　based　on　the　driver＇s　needs.　In　order　to　explore　the　method　of　HMI　optimization,　this　paper　builds　a　driving　simulation　experimental　test　platform　of　freeway　connected　vehicle　system,　designing　two　experimental　scenarios　according　to　the　technical　conditions　(without　HMI　or　with　HMI).　After　that,　the　paper　uses　Markov　chain　to　explore　the　drivers＇　fixation　transition　to　identify　the　driver＇s　needs　in　different　sections　at　different　conditions.　Besides,　combined　with　the　visual　trajectory　results,　the　paper　provides　suggestions　for　optimizing　HMI.　The　results　show　the　review　rate　of　drivers　in　each　section　for　the　straight　upper　front　area　is　very　high.　The　highest　value　occurs　in　the　heavy　fog　zone,　suggesting　that　HMI　can　provide　more　road　information.　HMI　should　offer　a　prompt　to　the　driver　before　entering　the　warning　zone,　and　remind　the　driver　of　changes　in　the　speed　limit　before　entering　the　fog　area.　The　prompt　module　of　HMI　should　shorten　the　horizontal　length.　In　conclusion,　this　paper　aims　to　propose　a　general　diagnosis　method　by　diagnosing　the　self-designed　HMI.