The　backdoor　attack　has　emerged　as　a　prevalent　threat　that　affects　the　effectiveness　of　machine　learning　models　in　intelligent　vehicles.　While　such　attacks　may　not　impair　the　normal　performance　of　the　trained　model,　they　can　be　exploited　by　malicious　entities　to　manipulate　model　inferences,　resulting　in　serious　problems.　In　this　paper,　we　design　a　dynamic　gradient　clipping　(DGC)　method　aimed　at　rectifying　backdoor　models　by　eliminating　the　underlying　backdoor　trigger.　Firstly,　we　construct　a　repair　dataset　fused　by　some　clean　samples　and　few-shot　backdoor　samples　to　amplify　the　backdoor　behavior　when　we　only　obtain　limited　backdoor　samples.　Subsequently,　we　introduce　sample　states　to　characterize　the　backdoor　behavior　of　the　target　model,　determined　by　the　model＇s　inference　outcome.　Finally,　we　devise　the　DGC　method　to　clip　parameter　gradients　at　varying　degrees,　effectively　eliminating　the　backdoor　trigger　within　the　target　model.　Through　the　evaluation,　the　simulation　results　demonstrate　that　our　DGC　method　exhibits　robust　defense　capabilities　against　four　contemporary　state-of-the-art　backdoor　attacks,　reducing　the　attack　success　rate　by　95%　with　only　0.1%　similar　to　4.8%　model　accuracy　loss.