There　will　be　a　time　when　automated　vehicles　coexist　with　human-driven　ones.　Understanding　how　drivers　assess　driving　risks　and　modeling　their　differences　is　crucial　for　developing　human-like　and　personalized　behaviors　in　automated　vehicles,　gaining　people＇s　trust　and　acceptance.　However,　existing　driving　risk　models　are　usually　developed　at　a　statistical　level,　and　no　single　model　can　accurately　describe　and　explain　the　variations　in　risk　perception　among　drivers.　We　propose　a　concise　yet　effective　model　known　as　the　Potential　Damage　Risk　(PODAR)　model,　which　provides　a　universal　and　physically　meaningful　structure　for　estimating　driving　risk　and　explaining　the　reasons　for　differences　in　risk　perception.　Leveraging　an　open-access　dataset　collected　from　an　obstacle　avoidance　experiment,　this　paper　establishes　individual　risk　perception　models　for　drivers　with　high　fitness　performances.　We　conclude　that　the　variations　in　risk　perception　among　drivers　stem　from　their　assessments　of　potential　damage,　accounting　for　the　uncertainty　in　both　temporal　and　spatial　dimensions.　Our　findings　offer　an　explanation　for　human　risk　perceptions　and　present　a　promising　risk　model　for　autonomous　vehicles　to　develop　human-like　behaviors　and　personalized　services.