The　sight　distance　reliability　of　LiDAR-based　automated　vehicles　(LAVs)　in　complex　road　environments　is　critical　for　their　deployment.　Road　geometry　and　weather　conditions　are　two　key　factors　affecting　the　perception　capabilities　of　LAVs.　However,　current　studies　rarely　analyzed　the　combined　effects　of　these　two　factors　on　sight　distance　performance.　This　study　investigates　LAVs＇　sight　distance　reliability　on　curved　roads　in　adverse　weather,　considering　various　design　speeds,　curve　radii,　and　scenarios　including　clear　weather,　rain,　and　fog.　Available　sight　distances　(ASDs)　are　extracted　using　defined　LiDAR　point　cloud　thresholds　to　develop　sight　distance　reliability　functions.　Moreover,　the　Monte　Carlo　simulation　quantifies　sight　distance　failure　risks　associated　with　different　levels　of　LAVs　in　varied　operational　contexts.　The　results　unveil　a　significant　impact　of　weather　conditions　on　ASDs,　highlighting　that　decreased　visibility　and　increased　rainfall　adversely　affect　ASD,　with　a　notable　56.64%　probability　of　sight　distance　failure　under　certain　conditions.　Additionally,　the　study　finds　that　shorter　perception-reaction　times　can　mitigate　sight　distance　risks　when　LAVs　navigate　on　curved　roads,　whereas　higher　speeds　exacerbate　these　risks.　Furthermore,　the　study　reveals　that　lower　automation　levels　struggle　to　maintain　adequate　sight　distances　on　existing　curved　roads　under　adverse　weather　conditions.　These　insights　remind　road　managers　to　determine　appropriate　speed　limits　for　LAVs　on　curved　roads,　enhancing　operational　safety　from　a　sight　distance　perspective.