As　a　key　component　of　point　cloud　acquisition　technology,　LiDAR　(Light　Detection　and　Ranging)　acts　as　an　eye　in　autonomous　driving.　The　vehicle-mounted　LiDAR　point　cloud　system　is　the　prerequisite　foundation　for　the　vehicle　to　be　able　to　perform　environmental　perception,　path　planning,　navigation,　and　other　behaviors.　In　practical　applications,　LiDAR　point　clouds　have　a　wide　distribution　range　and　a　large　amount　of　data,　which　poses　major　challenges　to　existing　transmission　and　storage　technologies.　Aiming　at　the　point　cloud　from　automotive　LiDAR　in　autonomous　driving,　we　propose　a　point　cloud　lossy　compression　framework　based　on　range　image　index　and　classified　sparse　sampling.　This　paper　takes　the　original　data　packet　as　the　compression　target　and　converts　it　into　a　2D　range　image　and　a　3D　point　cloud　based　on　its　data　characteristics.　On　this　basis,　we　can　segment　3D　point　clouds　into　ground　point　and　non-ground　point　cloud　by　applying　progressive　morphological　filters.　Then,　we　perform　de-redundant　sparse　processing　at　different　intensities　on　the　two.　A　sparse　2D　range　image　is　generated　by　referring　to　the　range　image　index　of　the　downsampling　3D　point　cloud　and　is　represented　as　a　more　compact　form　of　one-dimensional　range　vector　combined　with　its　corresponding　occupancy　image　and　Morton　sorting.　Finally,　the　image　coding　method　is　used　to　further　compress　the　point　cloud.　Experimental　results　show　that　our　proposed　method　is　better　than　the　existing　algorithms,　thereby　effectively　reducing　the　spatial　redundancy　of　the　point　cloud　and　achieving　higher　reconstruction　quality.　©　2021　IEEE.