This　article　focuses　on　the　development　of　a　stable　pedestrian　crash　avoidance　mitigation　system　for　autonomous　vehicles　(AVs).　Previous　works　have　only　used　simple　AV-pedestrian　models,　which　do　not　reflect　the　actual　interaction　and　risk　status　of　intelligent　intersections　with　manual　vehicles.　The　paper　presents　a　model　that　simulates　the　interaction　between　automatic　driving　vehicles　and　pedestrians　on　unsignalized　crosswalks　using　the　multi-agent　deep　deterministic　policy　gradient　(MADDPG)　algorithm.　The　MADDPG　algorithm　optimizes　the　PCAM　strategy　through　the　continuous　interaction　of　multiple　independent　agents　and　effectively　captures　the　inherent　uncertainty　in　continuous　learning　and　human　behavior.　Experimental　results　show　that　the　MADDPG　model　can　fully　mitigate　collisions　in　different　scenarios　and　outperforms　the　DDPG　and　DRL　algorithms.