Based　on　polynomial　theory　and　radial　basis　function　(RBF)　neural　network,　a　path　planning　method　for　the　intelligent　vehicles　lane　changing　process　was　proposed.　The　near-optimal　solutions　of　the　lane　changing　path　in　the　fixed　boundary　conditions　can　be　obtained　by　this　method.　In　this　method　the　lane　changing　vehicle　and　obstacle　vehicles　were　presented　by　rectangle,　and　then　in　the　constraints　of　collision　detect　conditions,　boundary　conditions　and　comfort　performance　index　that　the　near-optimal　solutions　of　the　lane　changing　path　were　calculated.　In　addition,　the　dynamic　RBF　neural　network　was　used　to　solve　the　problem　that　how　to　select　a　reasonable　boundary　conditions.　By　this　dynamic　RBF　neural　network　the　reasonable　boundary　conditions　were　calculated　and　the　neural　network　has　the　function　of　online　learning,　which　was　optimized　by　itself.　Simulation　results　prove　the　correctness　and　feasibility　of　this　algorithm,　and　illustrative　examples　show　the　advantage　of　this　new　method　in　the　case　of　lane　changing　with　multiple　obstacles.