With　the　continuous　development　of　autonomous　driving　technology,　how　to　accurately　and　efficiently　identify　the　target　in　some　challenge　scenarios,　e.g.　nighttime,　has　become　an　important　task.　Although,　in　view　of　cost,　pure　visual　object　detection　technology　has　become　popular　with　growing　camera　performance　and　image　processing　capabilities,　the　pure　visual　object　detection　technology　based　on　cameras　still　faces　enormous　challenges　in　accurately　identifying　targets　in　environments　with　poor　visibility,　such　as　dusk,　night,　or　rainy　or　foggy　days.　This　article　introduces　the　state-of　art　image　processing　technology,　i.e.　YOLOv8,　to　improve　target　detection　capability　in　the　challenge　environment.　The　vehicle　recognition　accuracy　of　YOLOv8　is　evaluated　in　low　light　environments,　such　as　dusk　and　night.　The　results　show　that＇s　the　recognition　and　detection　accuracy　of　YOLOv8　at　night　surpasses　previous　versions　of　the　series,　making　it　an　important　tool　for　improving　autonomous　driving　safety　in　poor　lighting　environments.　©　2023　IEEE.