Based　on　vehicle　trajectory　data　captured　by　unmanned　aerial　vehicle　(UAV)　video,　this　study　investigates　the　differences　in　the　behavior　pattern　distribution　of　different　lane　change　(LC)　maneuvers　in　the　weaving　area.　The　hierarchical　Dirichlet　process　and　hidden　Markov　model　(HDP-HSMM)　algorithm　is　first　used　to　segment　the　LC　sequences　to　generate　behavior　primitives　with　risk　attributes,　and　then　the　Gaussian　mixture　model　and　latent　Dirichlet　allocation　(GMM-LDA)　algorithm　is　used　to　cluster　the　primitives　to　obtain　the　behavior　patterns　that　make　up　the　LC　process.　The　results　show　that　the　composition　of　patterns　differed　significantly　between　consecutive　LC　and　left/right　LC,　and　that　the　proportion　of　riskier　patterns　was　relatively　high.　This　study　can　provide　a　basis　for　the　development　of　LC　models　for　autonomous　vehicles　and　early　warning　of　risky　LC　maneuvers　through　an　in-depth　analysis　of　the　patterns　of　LC　maneuvers　occurring　in　the　weaving　area.　©　ASCE.