Featured　Application　This　study　identifies　factors　influencing　Human-driven　vehicle　(HDV)　drivers＇　attitudes　toward　connected　autonomous　vehicles　(CAVs),　affirming　that　psychological　characteristics　significantly　impact　interactive　driving　choices.　The　findings　hold　vital　policy　implications　for　government　agencies　and　CAV　producers,　guiding　strategic　decisions　in　the　autonomous　transportation　landscape.Abstract　Human-driven　vehicles　(HDVs)　will　share　the　road　with　connected　autonomous　vehicles　(CAVs)　in　the　near　future.　Accordingly,　the　investigation　of　the　interactive　behavior　of　HDV　drivers　toward　CAVs　is　becoming　critical.　In　this　study,　a　questionnaire　survey　was　first　conducted.　The　heterogenous　clusters　of　HDV　drivers　were　revealed　through　the　latent　profile　analysis　based　on　the　collected　dataset,　with　the　focus　on　their　trust　and　familiarity　with　CAVs,　their　attitudes　towards　sharing　the　road　with　CAVs,　and　their　risk　perception　and　perceived　behavior　control　when　they　faced　the　CAVs.　Subsequently,　the　correlation　between　the　respective　latent　cluster　and　several　socio-demographic　factors　was　understood　based　on　the　multinomial　logistic　regression　model,　and　the　choice　behavior　of　each　cluster　in　different　interactive　driving　scenarios　was　revealed.　Three　vital　findings　were　reported.　(1)　Three　profile　clusters　of　HDV　drivers　(i.e.,　negative　individuals,　neutral　individuals,　and　positive　individuals)　were　revealed.　(2)　The　drivers　of　a　low/middle　income　and　with　a　long　driving　experience　were　more　likely　to　be　negative　individuals,　whereas　the　CAV　experience　can　make　drivers　feel　positive　towards　CAVs.　(3)　Negative　individuals　might　give　up　on　changing　lanes　when　a　CAV　platoon　driving　was　noticed　in　the　target　lanes;　in　addition,　they　might　raise　more　rigorous　requirements　for　vehicle　spacing　in　the　lane-changing　process　when　finding　CAVs　driving　in　the　target　lanes.　To　be　specific,　negative　and　neutral　individuals　preferred　driving　in　front　of　the　CAV　platoons.　The　findings　can　provide　references　for　developing　effective　management　measures　or　CAV　control　strategies　for　transportation　systems.