With　the　exponential　advancement　of　vehicle　networking　applications　and　autonomous　driving　technology,　the　demand　for　efficient　and　secure　autonomous　vehicles　(AVs)　is　increasing.　AVs　require　the　ability　to　gather　information　to　navigate　complex　and　ever-changing　traffic　environments,　making　effective　communication　with　other　vehicles　or　roadside　units　(RSUs)　crucial　for　achieving　co-awareness.　Integrated　Sensing　and　Communication　(ISAC)　technology　emerges　as　a　promising　solution　for　the　future　of　autonomous　driving.　However,　in　the　dynamic　and　uncertain　real-world　road　environment,　the　selection　of　sensing　and　communication　(SC)　functions　becomes　paramount　in　enhancing　performance.　Moreover,　ambient　noise　often　disrupts　the　interaction　between　vehicles　and　roadside　units,　leading　to　a　partial　loss　of　environmental　states.　To　address　this　challenge,　we　propose　a　novel　approach　for　selecting　sensing　and　communication　functions,　even　in　the　presence　of　partial　loss　of　environmental　states.　Specifically,　we　approximate　a　partially　observable　Markov　decision　process　(POMDP)　to　a　complete　Markov　decision　process　(MDP)　through　matrix　completion　and　subsequently　utilize　deep　reinforcement　learning　(DRL)　to　solve　it.　Additionally,　we　propose　a　matrix　completion　algorithm　based　on　the　alternating　direction　method　of　multipliers　(ADMM)　with　deep　unfolding　to　accurately　complete　the　missing　environmental　states.　Finally,　we　demonstrate　that　the　proposed　method　outperforms　the　other　POMDP-based　approaches　for　SC　function　selection　in　an　ISAC-enabled　vehicular　network.