Collaborative　perception　enables　autonomous　vehicles　to　exchange　sensor　data　among　each　other　to　achieve　cooperative　object　classification,　which　is　considered　an　effective　means　to　improve　the　perception　accuracy　of　connected　autonomous　vehicles　(CAVs).　To　protect　information　privacy　in　cooperative　perception,　we　propose　a　lightweight,　privacy-preserving　cooperative　object　classification　framework　that　allows　CAVs　to　exchange　raw　sensor　data　(e.g.,　images　captured　by　HD　camera),　without　leaking　private　information.　Leveraging　chaotic　encryption　and　additive　secret　sharing　technique,　image　data　are　first　encrypted　into　two　ciphertexts　and　processed,　in　the　encrypted　format,　by　two　separate　edge　servers.　The　use　of　chaotic　mapping　can　avoid　information　leakage　during　data　uploading.　The　encrypted　images　are　then　processed　by　the　proposed　privacy-preserving　convolutional　neural　network　(P-CNN)　model　embedded　in　the　designed　secure　computing　protocols.　Finally,　the　processed　results　are　combined/decrypted　on　the　receiving　vehicles　to　realize　cooperative　object　classification.　We　formally　prove　the　correctness　and　security　of　the　proposed　framework　and　carry　out　intensive　experiments　to　evaluate　its　performance.　The　experimental　results　indicate　that　P-CNN　offers　exactly　almost　the　same　object　classification　results　as　the　original　CNN　model,　while　offering　great　privacy　protection　of　shared　data　and　lightweight　execution　efficiency.