The　ability　to　predict　future　structure　features　of　environments　based　on　past　perception　information　is　extremely　needed　by　autonomous　vehicles,　which　helps　to　make　the　following　decision-making　and　path　planning　more　reasonable.　Recently,　point　cloud　prediction　(PCP)　is　utilized　to　predict　and　describe　future　environmental　structures　by　the　point　cloud　form.　In　this　letter,　we　propose　a　novel　efficient　Transformer-based　network　to　predict　the　future　LiDAR　point　clouds　exploiting　the　past　point　cloud　sequences.　We　also　design　a　semantic　auxiliary　training　strategy　to　make　the　predicted　LiDAR　point　cloud　sequence　semantically　similar　to　the　ground　truth　and　thus　improves　the　significance　of　the　deployment　for　more　tasks　in　real-vehicle　applications.　Our　approach　is　completely　self-supervised,　which　means　it　does　not　require　any　manual　labeling　and　has　a　solid　generalization　ability　toward　different　environments.　The　experimental　results　show　that　our　method　outperforms　the　state-of-the-art　PCP　methods　on　the　prediction　results　and　semantic　similarity,　and　has　a　good　real-time　performance.