Object　detection　is　essential　for　autonomous　vehicles　to　perceive　and　understand　their　environment.　The　restricted　storage　and　processing　capacities　of　vehicles　necessitate　the　outsourcing　of　object　detection　services.　However,　this　may　raise　concerns　regarding　the　privacy　of　the　uploaded　images.　Although　there　have　been　some　studies　on　privacy-preserving　object　detection　networks,　they　either　lack　location　privacy　protection　or　involve　excessive　computational　and　communication　overheads.　To　address　this　issue,　we　propose　a　private　object　detection　inference　framework　(PODI),　which　is　based　on　a　Faster　R-CNN　and　aims　to　protect　both　classification　and　location　privacy.　PODI　employs　additive　secret　sharing　protocols　to　support　collaborative　computation　between　two　edge　servers.　By　using　efficient　protocols　such　as　secure　Maxpool,　secure　array　access,　and　secure　exponent,　PODI　significantly　reduces　computational　and　communication　overheads.　A　theoretical　analysis　has　confirmed　the　security,　correctness,　and　efficiency　of　PODI.　Extensive　experiments　were　used　to　demonstrate　its　security,　inference　accuracy　comparable　to　plaintext　approaches,　and　lower　cost　of　secure　inference.　©　2024　Elsevier　B.V.